Istook letter expresses concerns over real estate; demands action

WASHINGTON (JR) -- The Republican chairman of a Congressional oversight committee had given the District of Columbia only days to react to strings contained within the city's federally approved budget.

Apparently not satisfied with the city's willingness to establish a real estate management code as prescribed in the budget, Rep. Ernest J. Istook, (R-Okla.), sent a letter to Mayor Anthony Williams demanding action. The letter, obtained by the Corridor Real Estate Journal, was dated Dec. 13 -- 15 days after President Clinton signed the budget and its riders into law.

He is "concerned," but not ready to "nitpick."

The budget rider mandates that the city better manage existing leases and property disposition as well as create an inventory of district-owned properties.

Williams in his Jan. 4 reply, also obtained by the Corridor Real Estate Journal, pointed out that progress is being made to satisfy the congressional concerns. In addition, the Washington City Council now is considering the Real Property Act to streamline the city's handling of its real estate assets.

In the three-page letter addressed to Williams, Istook raised concerns that 24 of the city's 68 leases have expired, putting the district in month-to-month rental situations and not allowing the city to get the biggest bang for the buck for long-term property rentals. This includes both city-owned properties leased to private tenants and privately owned properties leased by the city.

"While the committee believes the act's requirements will have a positive impact on the district's real estate programs, we are concerned about the proper implementation of the provisions," wrote Istook.

Istook also questioned whether the 68 leases reported by the city is the total number. "Steps should be taken to ensure that all district leases are included in any filings and that the abstracts and reports are complete," Istook said.

Another issue raised in the letter concerned the city's ability to manufacture a dependable and accurate inventory and to create policy guidelines to handle the surplus of district-owned properties.

"We are gravely concerned about the current status of the real property inventory and our recommendation is that you look in the matter personally," he said.

The letter goes on to say, "The committee understands that this project may have been halted by district officials. If this is true we are concerned that the lack of interest now being shown for the property inventory project may impede the proper implementation of this general provision."

Istook's letter was his own, but represented the position of the committee. However, Rep. James P. Moran Jr., D-Va., the ranking Democrat on Istook's subcommittee was unaware of the letter.

It is not unusual for a chairman of a committee to send such correspondence, said Paul Reagan, Moran's spokesman.

In response, Williams said that he already has begun to address the problems. Reforms "have already been set into motion." Williams wrote.

The mayor stated that of the 2.9 million square feet of space under lease only 642,115 square feet, 22 percent of the current inventory, is on a month-to-month rental.

"The maintenance of a small ratio of flexible space leasing should not be unexpected," said Williams. "The district over the past two years has been consolidating smaller leases into more centralized government centers. It is often necessary to maintain leases in a 30- day terminable status when implementing a consolidation."

Williams agreed in the letter that there may indeed be more than 68 total leases and authorized "all district entities with leasing authority to submit current leases with abstracts to the Office of Property Management."

The Real Property Act presently being crafted by council should deal with many of the deficiencies Istook cited.

"The Real Property Act," wrote Williams, "details the components required as part of the district's inventory of all its real property, including actual observation and evaluation of property condition. The collection, sorting, and examination of this information will serve as the foundation for asset management-based planning and decisions."

"It's pretty clear the folks on Capitol Hill were not aware of the progress of the management of real property assets," said Councilwoman Kathy Patterson, chair of the Government Operations Committee, which created the Real Property Act.

"We have been working for three or four years now to improve the way we manage real property and the legislation that is before the council right now (The Real Property Act) would be the next step in that reform effort," Patterson said.

She said that the measure is a program where the city will hire tenant reps to handle leases and also create of an Office of Property Management, which will streamline and revise the process for property maintenance and disposition.

The measure "applies an asset management approach to our real property so that all our property is regularly and routinely inventoried in four categories including underutilized, appropriately utilized and utilized," said Patterson.

Istook's press secretary, Micah Swafford, said, "This is the first step. The city agrees more needs to be done and gave us assurance that they are doing that. Things seem to be moving in a positive direction and so at this point we not ready to nitpick."

REBNY leads industry effort to combat terrorism threats

The Real Estate Board of New York (REBNY) continues to lead the real estate industry's effort to educate building owners and managers, as well as real estate brokers, about protecting their buildings from future terrorist attacks.

REBNY has been working with federal and city officials since 9-11 to help its members take preventative measures.

Members have participated in educational seminars as well as planning meetings with key officials to determine ways building owners and managers can ensure the highest level of safety and security in residential and commercial buildings.

Shortly after 9-11, in early 2002, REBNY held a meeting with the New York Police Department (NYPD) and the Federal Bureau of Investigation (FBI) to discuss best security practices, communication between the industry and government agencies, and ways to work together.

More than 750 residential and commercial building managers participated in seminars held in June 2002, including at least 400 on the residential side, that educated employees about what to look for, how to report suspicious behavior and to whom to report. The seminars, four sessions in total, were led by representatives of the NYPD Office of Counter Terrorism.

REBNY's efforts to inform its members of potential threats and how to prevent them has continued since the seminars were held in 2002, in partnership with the Real Estate Roundtable and other national real estate organizations.

REBNY sponsored a meeting with the Joint Terrorism Task Force, the Department of Homeland Security, and the NYPD Office of Counter Terrorism in May 2003 to discuss the real estate industry's role in homeland security. Representatives discussed the industry's partnership with the Department of Homeland Security to inform REBNY members of potential threats and how to prevent them. In April 2004, REBNY's Residential Management Council invited Lt. Christopher Higgins of the NYPD Terrorist Incident Prevention Unit to discuss terrorist threats in high-rise buildings.

The training seminars held in 2002 utilized declassified FBI documents to inform participants about specific plots by terrorists to collapse apartment buildings. Trainers referred to Al-Qaeda Manuscripts which detailed plans for operatives to rent apartments and attempt to burn buildings down.

Through the seminars, building owners, managers, and real estate brokers, learned to spot suspicious behaviors and how to report them to the proper authorities. Participants also learned to prevent the use of fraudulent documents, which may have aided terrorists in renting apartments.

Difficult to Show Properties and Utility Maximizing Brokers

Authors B r u c e G o r d o n , S e a n P. S a l t e r a n d
Ke n H . J o h n s o n
Abstract This article is the winner of the Real Estate and the Internet
manuscript prize (sponsored by PricewaterhouseCoopers)
presented at the American Real Estate Society Annual Meeting.
Brokers have long believed that difficult to show properties sell
at lower prices and take longer to sell. Where difficult to show
properties are defined as those properties that present
extraordinary difficulties for a broker in arranging or showing
the listing to a particular buyer. Buyers’ recent access to online
real estate applications may make the cost of avoiding these
properties prohibitive to brokers. Employing a hedonic pricing
model and duration modeling techniques, this study finds that
property price and marketing time are not significantly affected
for these properties. The results suggest that brokers possess
limited market power.
Introduction
The majority of properties that are listed and marketed by brokers are not sold by
the property’s listing broker. In most cases, the property in question is sold by a
cooperating broker, either from another firm or another broker within the listing
firm.1 The term ‘‘broker’’, though not technically correct, is used in this study to
represent all the licensed salespeople and associate brokers working within a given
Multiple Listing Service (MLS).
Today, most real estate firms, and their brokers that specialize in the selling of
residential properties, are members of a MLS. Properties marketed by a broker
are placed in the MLS and made available to all other member brokers. The listing
broker makes an array of information about the subject listing available to
potential selling brokers. This information includes, but is not limited to,
particulars such as the property list price, the number of bedrooms, number of
baths, car storage, school zones, exterior and interior amenities, kitchen features
and a listing broker comments.2 Showing instructions are also made available
through the MLS. These instructions typically include owner contact information
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and other information, including the presence of a pet, which is designed to
facilitate the showing of the property by potential cooperating brokers.
There is a long held belief among practicing brokers that, ceteris paribus, difficult
to show properties translate into lower prices and longer marketing times. Where
difficult to show properties are defined as those properties that present
extraordinary difficulties for a broker in arranging or showing the listing to a
particular buyer. The idea is simple and incorporates two long accepted economic
paradigms, utility maximization and the law of supply and demand. It is
hypothesized that in order to maximize their utility, showing brokers in effect
create two demand schedules. One schedule reflects the demand for properties that
are not difficult to show. A second schedule reflects the demand for difficult to
show properties.3 The second schedule is located inside the first, creating an
equilibrium price that is lower for difficult to show properties (see Exhibit 1).4 A
corollary to this hypothesis is an extension in property marketing time due to
fewer showings.
Equilibrium Price
Exhibit 1
Price
S
P’
P’’
DND
DD
Q’’ Q’ Quantity
Here DND and DD represent the demand schedules for properties that are not difficult to show and
properties that present difficulties in showing, respectively. The equilibrium price for not difficult to
show properties, P , is greater than the equilibrium price for properties that are difficult to show, P .
D i f f i c u l t t o S h o w P r o p e r t i e s 1 1 3
At one time or another all brokers have been cornered in a bathroom by an
unfriendly dog, chased an escaped cat, found it difficult to arrange a showing time
with an owner who requires an appointment be set in advance, had to drive across
town to pick-up a key for just one of many showings, etc. In addition, brokers
typically arrange several showings and delays at one showing often produce delays
at others. These problems are not trivial and, in fact, produce an increase in the
marginal cost of showing difficult to show properties. Therefore, selling brokers
view the marginal cost of including many of these difficult to show properties
in the feasible set of potential purchases as being greater than the properties’
marginal benefit from inclusion. Accordingly, these properties are dropped
from consideration and thus face a different demand schedule than less difficult
showings.
While this trade-held hypothesis might be true, there is another possible and
competing hypothesis. Specifically, the trade held hypothesis implicitly assumes
that showing brokers exert some significant level of market power over consumers
of real estate services. Where market power is operationally defined as the ability
of brokers to suppress otherwise competitive market forces, specifically many
sellers and buyers of a relatively homogenous product, which should yield a single
equilibrium price. Under this market power scenario, selling brokers can maximize
their own utility by avoiding difficult to show properties at the expense of market
participants resulting in price reductions and extended marketing times for difficult
to show properties. If on the other hand, the market power of these brokers is
limited, property price and marketing time may not be affected.5
A recent spate of articles, including but not limited to Baen and Guttery (1997),
Tuccillo (1997), Bardhan, Jaffee and Kroll (2000), Bond, Seiler, Seiler and Blake
(2000) and Jud, Winkler and Sirmans (2002), and, hint at a possible source for
limiting brokers’ market power. All of these articles address to one extent or
another the impact of the Internet on residential brokerage. Baen and Guttery along
with Tuccillo explicitly argue that the growing use of Internet real estate
applications reduce information and transactions costs, speed transactions and will
ultimately lead to lower commissions by reducing the demand for brokerage
services.
Today, the Internet provides buyers with numerous online real estate applications
that allow buyers to prescreen potential properties via searchable databases and
virtual tours, as well as to prequalify themselves with mortgage calculators.
Showing brokers, fearing that buyers may learn of potential purchases from
Internet listings of other broker listed properties and no longer being able to count
on the inefficient nature of media, such as classified advertising, could now find
it sensible to include difficult to show listings in the feasible set of showings
presented to buyers. Said another way, the rational utility maximizing broker,
seeing that the expected marginal benefit of showing difficult to show properties
is greater than the marginal cost of avoiding these properties, now includes them
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in the feasible showing set. In effect, this creates one demand schedule for all
properties and a single uniform equilibrium price no matter what the showing
instructions for a listed property (see Exhibit 2).6 The extended corollary from
this line of thought suggests that marketing time for these difficult to show
properties would not be impacted by their showing instructions.
Are the prices of difficult to show listings affected adversely? Does the marketing
time for these properties extend, relative to similar properties? Alternatively, are
market forces afoot, perhaps online real estate applications, which can change the
showing patterns of rational utility maximizing brokers? These are the questions
examined in this study.
Employing comparable sold data from the Montgomery, Alabama area, this study
investigates the efficacy of the above arguments. A contiguous area is sampled in
order to create a homogenous product and lower overall variability in the housing
product. A hedonic pricing model is developed to investigate the effect, if any, of
difficult to show listings on property selling price. In addition, duration modeling
techniques are used to investigate for ‘‘time on the market’’ effects. Three proxies
Market Forces on Equilibrium Price
Exhibit 2
Price
S
P
DAll Listings
Q Quantity
Market forces exert themselves and consumers view only one demand schedule creating one
equilibrium price.
D i f f i c u l t t o S h o w P r o p e r t i e s 1 1 5
are posited for difficult to show properties in the hedonic pricing model and in
the duration model. PETS, KEYINOFF and APP represent properties that have
pets, require key retrieval from the listing broker and require prearranged showing
times, respectively. Any one, or a combination of these three showing instructions,
increases the difficulty of arranging a showing for the broker. In the trade held
hypothesis, the proxies for difficult to show properties will sign negative and
significant in the pricing model and positive and significant in the duration model.
Conversely, if the competing hypothesis holds, the proxies for difficult to show
properties instituted as controls in the models should prove benign.
Literature Review
The literature is replete with studies that examine the impact of brokers on
residential property price. In the interest of brevity and exposition, this study does
not digress into a detailed discussion of these works. Interested readers can consult
Yavas (1994) and Benjamin, Jud and Sirmans (2000) for a detailed explanation
of this area of research.
The impact of brokers on property time on the market is less extensive. These
studies can be broken into two distinct categories based on the methodology
employed. Most early, and some more recent studies, attempted to model property
marketing time via ordinary least squares (OLS) estimation. For representative
studies employing OLS estimations, the reader should consult Belkin, Hempel and
McLeavy (1976), Janssen and Jobson (1980), Asabere, Huffman and Mehdian
(1993), and Allen, Faircloth, Forgey and Rutherford (2000), among others.
However, the estimation of time on market models is evolving. Duration modeling
techniques are becoming the standard. These techniques, though new and not
completely accepted, recognize certain deficiencies in employing OLS modeling.
For example, OLS modeling is ill suited because of non-normality of the error
term, which can lead to bias in the model’s estimates of coefficients. Kiefer (1988)
provides an excellent presentation on OLS verses duration methodology. For a
representative sample of works that employ duration modeling, the reader can
consult Yang and Yavas (1995), Jud, Seaks and Winkler (1996) and Johnson,
Salter, Zumpano and Anderson (2001), among others.
The literature virtually ignores questions concerning detailed MLS marketing
information. Haag, Rutherford and Thomson (2000) provide one exception to this
rule and investigate the impact of broker comments found in the remarks section
of MLS listing on property price and marketing time. The study finds that
negative, as well as some positive comments, are associated with a price reduction.
The authors suggest these results indicate that many comments offered by brokers,
in the remarks section of MLS listing, are more hype than substantive. To date,
however, there has not been an examination of property showing instructions on
either property price or marketing time. This study addresses this issue.
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Data and Methodology
Data
Two data sources provide the essential data for this study. The Montgomery Area
Association of Realtors MLS provides data on housing characteristics, such as the
number of bedrooms, baths, and other physical characteristics including the
variables of interest PETS (properties with pets), KEYINOFF (properties that
require a key to be retrieved to be shown) and APP (properties which require an
appointment time be arranged with the owner prior to showing). The Montgomery
County Tax Assessor Office provides the needed information on property size
(square footage) and age.
The original data set consisted of all conventional residential closings (2,716) that
occurred during the calendar year 1998 in Montgomery, Alabama. In order to
insure that the data contained a complete set of housing characteristics for each
observation, observations that did not appear in both databases were eliminated.
Next, obvious data entry errors from the MLS database such as negative time on
the market, zero bedrooms or baths, no indication of type of siding, etc were also
eliminated. In addition, given that this study is concerned with the impact of
difficult to show properties, newly constructed and vacant properties were
eliminated. Finally, in a further effort not to muddle the question of the market
being studied, any tenant occupied properties were also eliminated.
This results in a final database of 945 observations on which this study is
conducted. Descriptive statistics and a legend for the variable definitions are
presented in Exhibits 3 and 4, respectively.
Hedonic Pricing Model
In order to test for the impact of difficult to show properties on property price,
the following hedonic pricing model is specified.
LnSP LnAGE LnSQFT LnBED
0 1 2 3
LnBATH LEE LANIER CARVER
4 5 6 7
GAR CPT FP GB
8 9 10 11
SEPSHOW POOL DOUBOVN
12 13 14
EIFS PETS KEYINOFF
15 16 17
APP . (1)
18
D i f f i c u l t t o S h o w P r o p e r t i e s 1 1 7
Summary Statistics
Exhibit 3
Variable Mean Median SE Mean
SP 118,329 103,000 1,802
TOM 82.640 64.000 2.260
AGE 21.640 19.000 0.581
SQFT 1,833 1,706 17.800
BED 3.237 3.000 0.020
BATH 2.207 2.000 0.020
JD 0.516 1.000 0.016
LEE 0.335 0.000 0.015
LANIER 0.132 0.000 0.011
CARVER 0.016 0.000 0.004
GAR 0.289 0.000 0.015
CPT 0.231 0.000 0.014
DRIVE 0.480 0.000 0.017
FP 0.782 1.000 0.014
GB 0.307 0.000 0.015
SEPSHOW 0.306 0.000 0.015
POOL 0.133 0.000 0.011
DOUBOVN 0.094 0.000 0.010
EIFS 0.048 0.000 0.007
PETS 0.091 0.000 0.009
KEYINOFF 0.018 0.000 0.004
APP 0.119 0.000 0.011
N 945
N - PETS 86
N - KEYINOFF 17
N - APP 112
In this model, LnSP represents the natural log of the sales price. The regressors
LnAGE, LnSQFT, LnBED and LnBATH act as continuous predictors representing
the property’s age, square footage, number of bedrooms and number of bathrooms,
respectively. Location proxies are estimated through the use of four high school
zones. There are three indicator variables in the model: LEE, LANIER and
CARVER, which are equal to one if the property is located in the Lee, Lanier or
Carver High School zones, respectively. They are zero otherwise. The reference
indicator, which is omitted from the model, is Jefferson Davis High School (JD)
and is reflected in the constant term. The model also includes indicator variables
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Legend for Variable Definitions
Exhibit 4
SP Contract sales price of the property;
TOM Time on market in days;
AGE Age of the property;
SQFT Square footage of the property;
BED Number of bedrooms in the property;
BATH Number of baths in the property;
JD One if the property is in the Jefferson Davis school zone, zero otherwise;
LEE One if the property is in the Lee school zone, zero otherwise;
LANIER One if the property is in the Lanier school zone, zero otherwise;
CARVER One if the property is in the Carver school zone, zero otherwise;
GAR One if the property has a garage, zero otherwise;
CPT One if the property has a carport, zero otherwise;
DRIVE One if the property has a driveway only, zero otherwise;
FP One if the property has a fireplace, zero otherwise;
GB One if the property has a garden bath, zero otherwise;
SEPSHOW One if the property has a shower separate from the tub, zero otherwise;
POOL One if the property has a pool, zero otherwise;
DOUBOVN One if the property has a double oven, zero otherwise;
EIFS One if the property is clad in EIFS, zero otherwise;
PETS One if the property has pets, zero otherwise;
KEYINOFF One if retrieving key from listing broker is required, zero otherwise; and
APP One if setting an appointment is required, zero otherwise.
for three types of parking. GAR (garage) and CPT (carport) are specified explicitly
in the model, while DRIVE (driveway only) is specified implicitly in the base
term. GAR, CPT and DRIVE take on values of one if the property has garage,
carport or driveway only parking. Otherwise, the observation in question receives
a value of zero.
In addition to these conventional regressors, controls for varying levels of quality
among the differing properties are placed in the model. This is done through the
inclusion of five quality variables. FP (fireplace), GB (garden bath), SEPSHOW
(separate shower), POOL (in-ground swimming pool) and DOUBOVN (double
oven) are characteristic of higher-quality properties in the sample area and are
available for use in the model. Each of these indicators receives a value of one if
the quality characteristic is present and zero otherwise.
Johnson, Salter, Zumpano and Anderson (2001), employing an expanded version
of this data set, find that EIFS (exterior insulation and finish systems) is positive
D i f f i c u l t t o S h o w P r o p e r t i e s 1 1 9
and significantly related to SP (sales price). EIFS is an exterior siding that has
encountered a significant amount of bad press as of late. The siding, though
originally hailed for its superior insulation quality, has proven to be permeable to
water, which has in turn caused significant structural damage over time.
Seemingly, EIFS-clad properties should be discounted by the market. However,
given the presence of uninformed buyers in the test market and EIFS’s high
correlation with quality, the positive and significant relationship between EIFS and
price is not surprising. Accordingly, EIFS is placed in the model.
Finally, PETS, KEYINOFF and APP, which control for the presence of pets, key
retrieval by the showing broker and arranged appointments, respectively, act as
proxies for difficult to show properties in the pricing model. All else being equal,
any one, or a combination of these three categories of showing instructions,
increases the difficulty in showing for a broker, and lowers the broker’s expected
utility if included in the feasible set of potential purchases due to higher marginal
cost. If the offered trade held hypothesis holds, the coefficients of these predictors
will be negative and significant. If on the other hand, rational utility maximizing
selling brokers now find it beneficial to include difficult to show properties in the
feasible showing set, perhaps because of expanded Internet use, these predictors
will be nonnegative.7
Duration Model
As mentioned earlier, the standard for modeling a property’s time on the market
(TOM) has shifted from OLS modeling to employment of nonlinear techniques.
Often referred to as duration modeling, this methodology provides a measure of
the probability of time on the market for a property. The following Weibull
operational model is specified.
exp(X ) LnAGE 2LnSQFT 3LnBED
0 1
4LnBATH 5LEE 6LANIER
CARVER GAR 9CPT 10FP
7 8
11GB 12SEPSHOW 13POOL
14DOUBOVN 15EFIS 16PETS
KEYINOFF APP . (2)
17 18
The flexible nature of the Weibull allows the function to be either monotonically
increasing or decreasing. If the scale parameter ( ), which indicates duration
dependence, is greater than one, positive duration dependence is indicated. That
is to say, the probability of a property selling is increasing through time. If the
scale parameter is less than one, the chance of a property selling decreases through
time. Interestingly, a unique quality of the Weibull hazard function occurs when
the scale parameter equals one. If equals 1, the specified hazard function reduces
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to the exponential hazard function (i.e., the probability of a sale is constant through
time). The interested reader can consult Greene (1997) for a detailed discussion
on this topic, in addition to the works mentioned earlier in the Literature Review
section. All of the independent predictors in this model are as defined in the
hedonic pricing model.
If, as brokers suspect, difficult to showing properties lead to extended marketing
times, the regressors PETS, KEYINOFF and APP will be positive and significant.
Conversely, nonpositive results will suggest a single demand schedule and uniform
TOM no matter what the property showing instructions. Benign results will also
suggest limited market power on the part of brokers.
Empirical Results
Exhibits 5 and 6 formally report the findings of the specified pricing and duration
models. Exhibit 7 reports the results of a nonspecified OLS model of TOM as
Hedonic Pricing Model
Exhibit 5
Predictor Coef Std. Dev. T P VIF
Constant 8.742 0.214 40.880 0.000
LnAGE 0.030 0.010 2.990 0.003 1.4
LnSQFT 0.258 0.029 8.870 0.000 1.3
LnBED 0.351 0.043 8.110 0.000 1.3
LnBATH 0.450 0.033 13.680 0.000 1.5
LEE 0.027 0.017 1.610 0.107 1.2
LANIER 0.086 0.024 3.530 0.000 1.3
CARVER 0.147 0.060 2.450 0.014 1.1
GAR 0.217 0.018 11.730 0.000 1.4
CPT 0.105 0.019 5.630 0.000 1.2
FP 0.133 0.020 6.750 0.000 1.3
GB 0.067 0.019 3.470 0.001 1.5
SEPSHOW 0.135 0.020 6.770 0.000 1.6
POOL 0.090 0.022 4.050 0.000 1.1
DOUBOVN 0.071 0.026 2.710 0.007 1.1
EIFS 0.158 0.036 4.340 0.000 1.2
PETS 0.034 0.026 1.330 0.184 1.1
KEYINOFF 0.023 0.055 0.410 0.683 1.0
APP 0.014 0.023 0.630 0.531 1.1
945; Adj. R2 71.8; and Adj. R2
Notes: The dependent variable is LnSP. N 71.2.
D i f f i c u l t t o S h o w P r o p e r t i e s 1 2 1
Duration / Weibull
Exhibit 6
Predictor Coef Std. Error Z P
Intercept 3.162 0.853 3.710 0.000
LnAGE 0.022 0.040 0.560 0.576
LnSQFT 0.128 0.117 1.090 0.275
LnBED 0.173 0.182 0.950 0.340
LnBATH 0.180 0.127 1.420 0.157
LEE 0.140 0.068 2.060 0.040
LANIER 0.110 0.100 1.100 0.272
CARVER 0.297 0.240 1.240 0.216
GAR 0.060 0.076 0.790 0.428
CPT 0.021 0.076 0.270 0.786
FP 0.124 0.081 1.530 0.127
GB 0.053 0.078 0.680 0.494
SEPSHOW 0.170 0.080 2.140 0.032
POOL 0.018 0.089 0.200 0.842
DOUBOVN 0.103 0.106 0.970 0.331
EIFS 0.279 0.147 1.890 0.058
PETS 0.023 0.106 0.220 0.829
KEYINOFF 0.209 0.228 0.910 0.361
APP 0.059 0.094 0.630 0.528
1.122 0.023
Note: The dependent variable is LnTOM. The 95% CI of the scale parameter is 1.066 1.182
implying the Weibull distribution is preferable to the exponential distribution. N 945. The log
likelihood 1412.2.
well. The explanatory power (R2) of the pricing model is 71.8%, and the model
is highly significant, as indicated by its F-Statistic (not reported) of 130.95
.00011). The coefficients for the control variables, excluding the
(p-value
variables of interest, are all correctly signed and statistically significant. The
model’s variance inflation factors (VIF) are reported formally in Exhibit 5 and are
all within accepted levels of tolerance.
The continuous regressor (AGE) is negative and significantly related to SP as
expected. The remaining continuous regressors SQFT, BED and BATH are all
positive and significantly related to SP, again as expected. As in all hedonic
pricing models, there is a need to control for property location. Specifically, the
Jefferson Davis (JD) school zone is needed to represent the preferred school zone
in the study area. Consultation with local real estate professionals indicates that
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Duration / OLS
Exhibit 7
Predictor Coef Std. Dev. T P VIF
Constant 2.524 1.215 2.080 0.038
LnAGE 0.081 0.056 1.440 0.149 1.4
LnSQFT 0.112 0.165 0.680 0.497 1.3
LnBED 0.082 0.246 0.330 0.738 1.3
LnBATH 0.244 0.187 1.310 0.191 1.5
LEE 0.184 0.096 1.910 0.057 1.2
LANIER 0.052 0.139 0.370 0.711 1.3
CARVER 0.629 0.340 1.850 0.065 1.1
GAR 0.185 0.105 1.760 0.079 1.4
CPT 0.023 0.106 0.220 0.828 1.2
FP 0.184 0.112 1.650 0.100 1.3
GB 0.115 0.110 1.050 0.292 1.5
SEPSHOW 0.292 0.113 2.580 0.010 1.6
POOL 0.110 0.126 0.870 0.385 1.1
DOUBOVN 0.006 0.149 0.040 0.966 1.1
EIFS 0.335 0.206 1.620 0.105 1.2
PETS 0.017 0.146 0.110 0.910 1.1
KEYINOFF 0.191 0.315 0.610 0.545 1.0
APP 0.087 0.131 0.670 0.505 1.1
945; R2 3.5; and Adj R2
Note: The dependent Variable is LnTOM. N 1.6.
the general preference ordering of school zones are Jefferson Davis, Lee, Lanier
and Carver, respectively. Consequently, these regressors should sign negative with
increasing orders of magnitude for the zones CARVER, LANIER and LEE,
respectively. These are the results. In addition, the coefficients for the location
proxies are all significantly related to SP.
Driveway only (DRIVE) is specified as the base case for parking type. Properties
with driveway only parking should be the least preferable, while garage parking
should be the most preferable. Thus, the coefficients for GAR and CPT should be
positive and significantly related to SP, with GAR having the greater magnitude.
Again, these results are found.
Turning to the model’s dichotomous controls, the regressors for the presence
of a fireplace (FP), garden bath (GB), separate shower (SEPSHOW), in-ground
swimming pool (POOL), double oven (DOUBOVN) and exterior insulation and
finish systems (EIFS) are all positive and significantly related to SP as expected.
D i f f i c u l t t o S h o w P r o p e r t i e s 1 2 3
Finally, this study examines PETS, KEYINOFF and APP for their impact on
property price. Interestingly, none of these variables are significant. These results
tends to discredit the popular belief among brokers of lower prices for difficult to
show properties and lend support to the competing hypothesis. These results also
have practical implications. Specifically, when confronted with a seller who does
not want to board their pets or insists on being difficult about their property’s
showing arrangements, listing brokers no longer need to worry about lower sales
prices and hence commissions. Selling brokers, on the other hand, would be well
advised to market all properties equally, irrespective of their perceived difficulties
in showing.
This study’s duration model is examined next. A 95% confidence interval on the
scale parameter, 1.066 1.182, suggests positive duration dependence and
that the Weibull model is preferred to the exponential model. The model indicates
three statistically significant factors affecting selling time. Properties located in
the Lee school zone sell significantly faster than properties in other location
proxies. This result is somewhat surprising, given the speculation by brokers in
this market that the JD location proxy is preferable. Interestingly, when combining
this result with the pricing model, this study finds, that while properties located
in the JD school zone receive a pricing premium, they take longer to sell on
average than properties located in the Lee school zone. These combined results
may be indicative of a shift in demand patterns and should prove helpful to local
brokers in the Montgomery area. The control for SEPSHOW is also significant
and negative. This result is expected, as a separate shower is a preference item
among buyers in the study area leading to a shorter selling time.
EIFS on the other hand, is statistically significant and positive, suggesting that
properties with EIFS take longer to market. This result, when combined with the
pricing model, is consistent with the hypothesis put forth in Johnson, Salter,
Zumpano and Anderson (2001) and is not surprising. Specifically, sellers in this
market, perhaps on the advice of their brokers, perceive of the existence of
uninformed EIFS buyers. Therefore, sellers of EIFS-clad properties do not
discount their price and wait for an uninformed buyer. The end result is a
nonnegative pricing impact from the presence of EIFS, but property marketing
time is extended. These results suggest that while sellers of EIFS-clad properties
may not receive a discount in their prices; the present value of their final proceeds
is less due to extended marketing time.8
All three of the proxies for difficult to showing properties are insignificant. These
results suggest that the long held belief of extended marketing times for difficult
to show properties is not warranted.9 Again, these findings lend support to the
competing hypothesis, suggesting limited marketing power on the part of brokers.
Conclusion
This study seeks to examine two competing hypotheses. Under both hypotheses,
selling brokers use a marginal cost benefit analysis to determine which properties
J R E R Vo l . 2 3 N o s . 1 / 2 – 2 0 0 2
1 2 4 G o r d o n , S a l t e r a n d J o h n s o n
to include in the set of feasible purchases presented to buyers. The first hypothesis
argues that brokers, without scientific investigation, believe that difficult to show
properties receive lower prices while witnessing extended marketing times due to
the increase in the marginal cost of presenting difficult to show properties to
buyers. Two different demand schedules are then presented with the demand
schedule for difficult to show properties being inside the demand schedule for
properties that do not present difficulties in showing. Ceteris paribus, the resulting
equilibrium price for difficult to show properties is lower than the price for other
properties. A corollary to this hypothesis is that fewer buyers will visit these
properties thus extending their marketing time. Significant market power on the
part of brokers is a necessary condition for this hypothesis to hold.
The competing hypothesis suggests that the casual beliefs of brokers may not
hold. Specifically, it may be possible that, due to online real estate applications
such as buyer searchable databases of available properties, virtual tours and
mortgage calculators, brokers may not be able to effectively maximize their own
utility by avoiding difficult to show properties. Under this scenario, a single
demand schedule is present resulting in uniform pricing. A corollary to this
competing hypothesis is that difficult to show properties should not witness
extended marketing times. This would be true if brokers possess limited power
over the market.
Using comparable sold data from the Montgomery, Alabama area, three categories
of difficult to show properties are classified. PETS, KEYINOFF and APP represent
properties that have pets, require key retrieval from the listing broker, and require
prearranged showing times, respectively. Any one, or a combination of these three
categories, increases the difficulty of a sale for the broker. These proxies for
difficult to show properties are specified in a hedonic pricing and duration model
to test for their impact on property price and marketing time.
The statistical results indicate that none of the proxies for difficult to show
properties influence either property price or marketing time. These results suggest
that brokers have limited market power, at least less than previously suspected,
and the casual beliefs of brokers concerning property price and marketing time
for difficult to show properties is misplaced. Buyers are now informationally
empowered as never before, and the logical cause for this reduction in market
power of brokers appears to be the Internet. In addition, the results have practical
applications. Past perceptions of difficult sellers causing longer marketing times,
lower sales prices and thus lower commissions for listing brokers, do not seem to
hold. Selling brokers, on the other hand, would be well advised to show all
properties irrespective of any showing difficulties involved.
A few words of caution seem warranted. First, it would be beneficial to test if the
casual beliefs of brokers ever held. Specifically, did difficult to show properties
experience a price discount and extended time on the market prior to recent Web
technologies? Second, it may be possible that the ratio of difficult to show
properties to properties that do not present difficulties in showing could alter the
D i f f i c u l t t o S h o w P r o p e r t i e s 1 2 5
results of this study. Said another way, the marginal analysis performed by brokers
to determine the feasible set of alternatives presented to buyers could vary
depending on the makeup of available properties. Third, a comparison of the
number of showings across difficult and alternative listings could provide
additional insight. Unfortunately, the data needed for these additional tests is not
available. All comparable data prior to 1998 has been purged from the
Montgomery area MLS system for data storage reasons, making the first two
suggestions impractical. A count of the number of showings for the differing
properties would be possible if all of the listings in the test area were equipped
with electronic lockboxes. This recent technology is becoming available in some
markets but was not available in 1998 for the test market. Taking all of these
limitations into account, the findings in this study, while suggestive of limited
market power on the part of brokers, need additional research to be confirmed.
Notwithstanding these concerns, the empirical results of this study strongly
indicate that difficult to show properties do not experience either a discount in
price or extended marketing time.
Endnotes
1
The data employed for this study contains 945 comparable sales. The listing and selling
broker were the same individual in only 78 cases or approximately 8.3% of the total
number of observations. Consultations with brokers in other markets revealed that, at
least casually, this rate of ‘‘double dipping,’’ as it is often referred to in the trade, seems
reasonable. Quite simply, listing brokers, though possessing certain synergistic
advantages, are overwhelmed by the vast number of competing cooperating brokers in
the market. Finally, the term listing broker, when used in this study, represents the broker
who has listed the property. The terms selling, showing or cooperating broker represents
a broker aiding buyers in locating property.
2
In fact, it is not uncommon for an MLS to have upwards of 300 profiled features available
for any listing. A listing of all of these features would prove exhaustive and in the interest
of brevity and space is omitted.
3
This second demand schedule can be thought of as a tax that has been placed on the
seller of property. The resulting tax incidence debate is another research question worthy
of interest. The dynamics of this question are not so straightforward, however, because
listing and selling brokers interest are not so perfectly aligned. Therefore, this additional
issue is set aside for the moment and addressed in future research in order to concentrate
on the question at hand. Said another way, this work makes the simplifying assumption
of no conflict of interest between the listing and selling brokers.
4
An alternative explanation for this trade held hypothesis is that the supply of properties
is perfectly inelastic in the short run. Exhibit 1 obviously takes a long run view of the
market. The results, however, are robust no matter the elasticity of the supply schedule.
Specifically, P’’ is lower than P’ regardless of the time frame considered for the model.
5
Frew (1987) provides an alternative explanation. Frew argues that brokers, seeking to
maximize their income, may ‘‘holdback’’ prime listings. This strategic move on the part
of listing brokers could easily influence the resulting price and time on market
estimations. However, ‘‘holdbacks’’ are inextricably intertwined with the tax incidence
question outlined in Endnote 3 and are reserved for future research.
J R E R Vo l . 2 3 N o s . 1 / 2 – 2 0 0 2
1 2 6 G o r d o n , S a l t e r a n d J o h n s o n
6
A point of clarification is perhaps warranted. Some may question the demand side impact
in both Exhibits 1 and 2 as opposed to a supply side effect. This is so because the focus
here is on the rational behavior of brokers who work with buyers and hence impact
demand. Listing brokers impact the market via the supply schedule. Their rational utility
maximizing behavior is held in check in order to investigate the demand side effects in
isolation. See Endnotes 3 and 5 for further explanation.
7
With thanks to an anonymous reviewer, it may be the case that high probability of sale
properties are significantly and positively related to difficult to show properties suggesting
some degree of selection bias. This study, however, explicitly holds all other factors,
including a property’s inherent probability of sale, which might influence a listing’s
marginal cost equal. However, future research concerning difficult to show properties
should investigate this potential bias.
8
Johnson, Salter, Zumpano and Anderson (2001) find EIFS-clad properties on average
experience extended marketing times of an additional 28 days. The average cost of
replacing EIFS siding and any necessary structural damage was not available for the
study. However, conversations with local brokers, home inspectors and contractors
indicated that these repairs far exceeded any lost value in proceeds due to extended
marketing time.
9
Alternate versions of both pricing and duration models were specified, and the results
remained consistent across all versions. Difficult to show properties did not suffer a
pricing discount, and they did not experience extended marketing times.
References
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Performance in the Housing Market, Working Paper, 2000.
Asabere, P. K., F. E. Huffman and S. Mehdian, Mispricing and Optimal Time on the Market,
Journal of Real Estate Research, 1993, 8:1, 149–56.
Baen, J. S. and R. S. Guttery, The Coming Downsizing of Real Estate: Implication of
Technology, Journal of Real Estate Portfolio Management, 1997, 3, 1–18.
Bardhan, A., D. Jaffee and C. Kroll, A Research Report: The Internet, E-Commerce and
the Real Estate Industry, Fisher Center for Real Estate and Urban Economics, Hass School
of Business, University of California Berkley, 2000.
Belkin, J., D. J. Hempel and D. W. McLeavy, An Empirical Study of Time on Market
Using Multidimensional Segmentation of Housing Markets, Journal of the American Real
Estate and Urban Economics Association, 1976, 4:1, 57–75.
Benjamin, J. D., G. D. Jud and G. S. Sirmans, Real Estate Brokerage and the Housing
Market: An Annotated Bibliography, Journal of Real Estate Research, 2000, 20:1/2, 217–
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Bond, M. T., M. J. Seiler, V. L. Seiler and B. Blake, Uses of Websites for Effective Real
Estate Marketing, Journal of Real Estate Portfolio Management, 2000, 6: 203–10.
Frew, J. K., Multiple Listing Service Participation in the Real Estate Brokerage Industry:
Cooperation or Competition, Journal of Urban Economics, 1987, 21:3, 272–86.
Greene, W. H., Econometric Analysis, Third edition, Upper Saddle River, NJ; Prentice-
Hall, 1997.
Hagg, J. T., R. C. Rutherford and T. A. Thomson, Real Estate Agent Remarks: Help or
Hype, Journal of Real Estate Research, 2000, 20:1/2, 205–215.
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Janssen, C. T. L. and J. D. Jobson, On the Choice of Realtor, Decision Science, 1980, 11,
299–311.
Johnson, K. H., S. P. Salter, L. V. Zumpano and R. I. Anderson, Exterior Insulation and
Finish Systems: The Effect on Residential Housing Prices and Marketing Time, Journal of
Real Estate Research, 2001, 22:3, 289–311.
Jud, G. D., T. G. Seaks and D. T. Winkler, Time on the Market: The Impact of Residential
Brokerage, Journal of Real Estate Research, 1996, 12:3, 447–58.
Jud, G. D., D. T. Winkler and G. S. Sirmans, The Impact of Information Technology on
Real Estate Licensee Income, Journal of Real Estate Practice and Education, 2002, 5:1,
forthcoming.
Kiefer, N. M., Economic Duration Data and Hazard Functions, Journal of Economic
Literature, 1988, 26:2, 646–79.
Tuccillo, J. A., Technology and the Housing Markets, Business Economics, 1997, 32, 17–
20.
Yang, S. X. and A. Yavas, Bigger is not Better: Brokerage and Time on the Market, Journal
of Real Estate Research, 1995, 10:1, 23–33.
Yavas, A., Economics of Brokerage: An Overview, Journal of Real Estate Literature, 1994,
2:2, 169–195.
The authors would like to thank Jim Frew, Don Jud, James Larsen and Daniel Winkler,
among others, for their insightful comments and suggestions provided at the 2001
ARES conference. In addition, we extend our thanks to the Alabama Real Estate
Research and Education Center for its continued support.
Bruce Gordon, University of North Alabama, Florence, AL 35632 or bgordon@
unanov.una.edu.
Sean P. Salter, University of Southern Mississippi, Hattiesburg, MS 39406 or
spsalter@earthlink.net.
Ken H. Johnson, Florida Atlantic University, Boca Raton, FL 33431 or johnson3@
fau.edu.
J R E R Vo l . 2 3 N o s . 1 / 2 – 2 0 0 2

Bankers take harder look at real estate portfolios; increased vigilance applies to new and existing loans - Los Angeles-area banks

Increased vigilance applies to new and existing loans

Los Angeles-area banks have started taking harder looks at their commercial real estate portfolios, according to a number of industry consultants.

"The banks have seen problems arise with commercial real estate in other parts of the country, so they are exercising greater care here," explained Larry Caddle, senior manager at the downtown Los Angeles office of Deloitte & Touche.

Despite the relatively resilient real estate market in Southern California, banks have begun to view the market as slowing, with rising vacancy rates and overdevelopment in some markets, Caddle said.

Consequently, these banks have stepped up monitoring of their commercial real estate borrowers as well as required more disclosure from them on an ongoing basis, according to Douglas McEachern, partner at Deloitte & Touche.

The increased vigilance applies to both existing loans and new loans, said Barry Rubens, CEO of Santa Monica-based California Research Corp.

While greater caution is a good sign for those who think banks have discarded some basic tenets of banking, throwing caution to the wind, it could mean greater costs associated with a given loan, McEachern said. These costs could well be passed onto the borrower in increased fees or higher interest rates, if they have yet been factored into either, he cautioned.

David Martins, chief accountant at the Office of Thrift Supervision, dismisses the significance of this cost factor for the average borrower but suggests that credit will become tougher to find for the marginal borrower. "Borrowers with good credit should continue to have no problem," he said.

Martins pointed out that credit availability has already become tighter under the Financial Institutions Reform, Recovery and Enforcement Act of 1989 (FIRREA), the savings and loan association bailout law, because of stricter capital standards and loan to one borrower limitations.

Under FIRREA's risk-weighted capital requirements, commercial real estate loans have become more expensive for banks as well as thrifts, McEachern indicated.

In their increased conservatism, banks are also avoiding commercial real estate loans which are strictly equity-based -- loans based on the value of the real property itself, Caddle said. Rather, banks are looking to the cash flow of the project, the creditworthiness of the borrower and other credit factors, he explained.

The initiative for the heightened bank diligence comes from the highest levels in the banks, Caddle commented.

"Senior management is trying to understand their portfolio better -- the types of loans in their portfolio and the markets they are in," Caddle said.

Senior management, in turn, is feeling the heat from regulators. "The regulators want management of the banks to control their institutions so that they can monitor management," said Caddle.

The activist regulators in this area include both the Office of the Comptroller of the Currency and the FDIC. Both are particularly concerned about senior management's accountability for the loan portfolios of their banks, Caddle said.

"The regulators want to make sure borrowers can repay the loans at the end of their terms and not simply rollover or reschedule the loans," Rubens said.

Additional pressure is also being exerted by the public accounting firms, Rubens indicated. Hit by a multitude of law suits from their lax auditing of savings and loans associations, the accounting firms have become more conservative in their auditing of banks, he explained.

The increased vigilance does not mean profits have dried up in commercial real estate, even with the departure of many battered savings and loan associations from the field.

In fact, many banks still have a huge appetite for commercial real estate loans, Caddle said. Rubens, a board member of Santa Monica-based Columbia National Bank, mentioned that the bank is currently looking for loans.

Although banks have stepped up their review of all kinds of commercial loans, certain types are likely to bear the brunt, including small strip shopping centers, minimalls and small office buildings, according to Rubens. These are all areas that have experienced reckless overbuilding in recent years,

These kinds of loans may also lose out because banks, insurance companies and mortgage companies -- the most active remaining players in the commercial real estate field -- tend to steer away from smaller projects, Rubens added.

Price Looks to Pad Property Portfolio As Sales Suprass $4 Billion Mark

LOS ANGELES -- Price Club is currently in the running to build an 800,000-square-foot retail sitein Burbank, Calif., that would provide the company with its largest development to date.

The deal, if successful, would add to Price Club's already impressive real estate portfolio: the retailer currently owns 37 of its o9 membership warehouse club sites.

Price Club also reported that its sales eclipsed the $4 billion mark for the just-completed fiscal year, ending Aug. 28. Volume increased more than 25 percent to $4.05 billion, as net income gre 29.2 percent to $94.8 million.

For the fourth quarter, sales increased 25.3 percent to $985.6 million, while net income rose 26 percent to $24.1 million in the quarter. Comparable sales increased 12.97 percent in the fourth quarter and 5.52 percent in the full year.

Price Club currently operates 40 warehouses in the United Sttaes and three locations in Canada through a joint venture with Steinberg Inc.

In addition to its own retail real estate holdings, Price Club has an interest in numerous other properties through P&K Associates--a joint venture created in 1985 between The Price Co. and Kornwasser and Friedman Shopping Center Properties. The 41-acre Burbank site currently under consideration would be developed by P&K.

P&K currently is planning, developing or managing some 30 real estate projects. According to the company, these holdings total more than 7.8 million leasable square feet and are valued at $501 million, with total financing for the projects being generated internally.

At the Burbank site, P&K plans to develop a two-level retail center that would include a 120,000-square-foot Price Club membership warehouse, a home center, several off-price apparel chains, a movie theater complex, restaurants, a hotel and a community center. Total leasable square footage would be 708,700 square feet.

P&K's proposed development, called the Burbank Promenade, is one of six under consideration by the city's redevelopment agency.

In its proposal, P&K said the Promenade would have a festival marketplace ambience similar to Faneuil Hall in Boston, combined with a regional shopping flavor resembling the Potomac Mills complex in Washington, D.C.

P&K has already lined up several retailers interested in the project, with the list including off-pricers Nordstrom Rack, Marshalls and T.J. Maxx, along with home furnishings retailer Home Express and home center operator Home Depot. P&K also anticipates that its project would draw either a Toys "R" Us or SportMart.

The Price Club warehouse would be the chain's second in Burbank. The company pegged anticipated first full-year sales at between $120 million and $150 million. For the entire development, P&K expects the proposed Promenade to generate sales of between $273.5 million and $365.7 million.

A final decision by the city for the Burbank development is not due for several months. If chosen, P&K said it would take between two and three years to build the Promenade.

Meanwhile, Price Club's bid for additional real estate, through the purchase of grocery wholesaler Alfrred M. Lewis, has been revised. Following closer review of Lewis, Price Club has downgraded its offer from $40 a share or a total price of $52 million, to $37 share or a total price of $48.1 million.

Price Club said it has begun a tender offer and upon receiving at least 70 percent of Lewis' outstanding shares, along with regulatory approval, the acquired company will be merged with a Price Club subsidiary. The deal would give the company five large warehouse/distribution centers in California, Nevada and Arizona--ranging from 171,000 to 483,000 square feet. The chain would also take over 35 small cash and carry wholesale food distribution outlets.

Property & Portfolio Research Announces Link to Trepp CMBS Analytics on Bloomberg

Property & Portfolio Research, Inc. (PPR) is pleased to announce a joint arrangement with Trepp,
LLC, the leading provider of data and analytics on Commercial Mortgage Backed Bonds
(CMBS). Users of Trepp CMBS Analytics on BloombergTM can now incorporate PPR's real
estate performance forecasts into their analyses of CMBS.
PPR's market insights enable users to quickly analyze how the individual market cycles affect the
source of the cash flows — the collateral. Rather than relying on broad statistical analysis, users
of the linked Trepp-PPR service can perform a loan-by-loan, credit-based analysis that will
enable investors to: (1) more accurately analyze default, prepayment, and extension risk, (2)
stress test bonds under different economic scenarios and analyze the impact on lives and pricing,
and (3) quickly differentiate bonds and exploit mispricing opportunities.
George Pappadopoulos, PPR's Director of Risk Management & Debt Research, noted, “These
insights even further enhance the ability of Trepp users to buy and sell smarter.”
Dan Gottlieb, Vice President of Trepp, said, “The seamless integration of PPR's forecasts in
Trepp CMBS Analytics on BloombergTM allows clients to perform more robust scenario analysis,
which goes well beyond CDR and CPR approaches.”
With offices in Boston and London, Property & Portfolio Research provides independent real
estate research and portfolio strategy services to the institutional real estate community. PPR
works with investors in real estate to help them meet their portfolio performance and risk
management goals. PPR tracks performance in 60 U.S. cities and 5 property types (office, retail,
warehouse, apartment, and hotel). The firm’s clients include: pension funds, Wall Street firms,
investment advisors, insurance companies, commercial banks, public companies, family offices,
and private capital sources — domestic and international, concerned with debt and equity
transacted in public and private markets.
Trepp, LLC, located in New York City, is a leading provider of CMBS analytics, data, software,
and consulting in the securities and investment management industry. Trepp serves the needs of
both the primary and secondary CMBS markets by providing, through Trepp CMBS Analytics on
BloombergTM, the largest commercially available trading quality CMBS Deal Library. In addition
Trepp is the primary provider of CMBS cashflows which are redistributed by BloombergTM daily
through its 130,000 terminals worldwide. The firm also offers subroutine libraries that can be
embedded into client's proprietary software applications, as well as an extensive array of
consulting services. Clients include the world’s largest broker dealers, commercial banks, CMBS
conduits, investment grade money managers, asset managers, and CMBS B-piece buyers.

HISTORIC TURNING POINTS IN REAL ESTATE

Historic Turning Points in Real Estate
Abstract
This paper looks for markers of ends of real estate booms or busts. The changes in market
psychology and related indicators that occurred at real estate market turning points in the
United States since the 1980s are compared with changes at turning points in the more
distant past. In all these episodes changes in an atmosphere of optimism about the future
course of home prices, changes in public interpretation of the boom, as well as evidence
of supply response to the high prices of a boom, are noted.
Robert J. Shiller
Cowles Foundation for Research in Economics
And International Center for Finance
Yale University
30 Hillhouse Avenue
New Haven CT 06520-8281
robert.shiller@yale.edu
2
Historic Turning Points in Real Estate 1
By Robert J. Shiller 2
By some accounts, the greatest challenge for economic forecasters is to predict
turning points. It is easy to extrapolate time series. It is less easy to tell when the series
will abruptly change trend and enter a different pattern or regime.
Figure 1 shows a chart of US stock price and home price indices since 1987. It
shows the Standard & Poor 500 Stock Price Index, a measure of the aggregate stock
market, which has been the mostly widely used broad gauge of the market since the index
was created in 1957. It also shows the Standard & Poor/Case-Shiller Composite Home
Price Index, a ten-city average which is a measure of the aggregate market for single
family homes, and is based on indices that Karl Case and I created in 1988. This index is
now used for futures and options contracts at the Chicago Mercantile Exchange and for
forward contracts in the over-the-counter market. Since the home price index is a three-
month moving average, the S&P 500 is also plotted as a moving three-month moving
average so that the two series are comparable.
The eye naturally picks out what appear to be major historic turning points.
Looking at the chart, one sees that the stock market has shown abrupt changes in regime
1
Presidential Address, Eastern Economic Association, 33rd Annual Conference, New York NY, Feb 23,
2007. The author is indebted to William Smalley for research assistance.
3
in early 2000, when it left a strong bull market, and went into rapid declines, and in late
2002 or early 2003, when it resumed a new strong upward climb.
The real estate market changed its direction markedly around 1990, from a
booming market to a market in the doldrums for the better part of a decade, and then the
market started accelerating upwards at increasing rates. The national home price boom
since the late 1990s appears unprecedented in US history, although the “baby boom” in
housing of the late 1940s and early 1950s comes close, and there have been some very
large local booms. The rate of US housing appreciation slowed after 2005, and, to some
eyes at least, it would appear just sometime after mid 2006, we are entering a new regime
of downward price changes.
These, then are the natural questions for today: How shall we think about these
major turning points of the past? Can study of these turning points offer to us any way to
predict when the upward trend in the stock market? Is there any way to decide whether
we really are entering a new regime of real estate market price declines?
Some people who think in terms of time-series analysis may disagree that such
questions should even be asked. Prices observed in the stock market are widely described
as random walks, if only approximately. If a time series is a true random walk, of a kind
that may be generated by a random number generator, then it will be seen to have
occasional major “turning points” that one could say have no other explanation than the
chance arrival of a string of negative shocks after that point. Probability theorists can
calculate the probability that the random walk will surpass the peak again, or calculate
the improper spectral density of the random walk, but they would not seek to “explain”
the turning point.
4
Of course, even if an economic time series is found to have the stochastic
properties of a random walk, we could still “explain” the turning point by interpreting the
sequence of shocks to the random walk that allow us afterwards to choose a point as the
turning point. We might be able to tell a story about the causes of the sequence of
negative shocks that came afterwards, which of course were really not generated by a
random number generator but instead have interpretations in terms of various historical
events. But, so long as the series is truly a random walk, the explanation would have to be
entirely after the fact, and would offer no insights into the future forecasting of turning
points.
Stock prices are not known to be exactly random walks. It has been demonstrated,
for example, that stock prices have shown some momentum through time. Certain models
very different from a random walk, involving such things as sudden regime changes, are
not easy to reject statistically. There is a substantial econometric literature that documents
deviations from random walk properties, and there is an econometric literature on the
identification of regime change in time series. But stock prices are fairly close to random
walks.
While stock prices somewhat resemble random walks, real estate prices certainly
do not. There is, in fact, a very obvious smoothness to home prices historically, as can be
seen from Figure 1. In fact, if one fits a quartic polynomial to the home price series
shown in the figure one gets an R squared, as a measure of closeness of approximation, of
99.6%. Of course one should not use the fitted polynomial as a forecasting device, but the
goodness of fit does illustrate the smoothness of the series, which no doubt has something
to do with the difficulty that professionals and speculators have in reacting to new
5
information about the housing market. The housing market is populated mainly ordinary
folk who do not react with the speed of professionals.
A polynomial has no unambiguous turning points. The point at which the slope
changes from positive to negative or negative to positive may stand out to the eye on a
plot of the polynomial, but the same point would not stand out on a plot of the slope of
the derivative of the polynomial. Indeed, Figure 2, which shows the annualized rate of
change of the same monthly series that appears in Figure 1 along with the National
Association of Home Builders Index of Traffic of Prospective Buyers, gives a somewhat
different impression where turning points might lie. There is of course seasonality, hard
to detect on Figure 1, but which stands out as a powerful annual oscillation in Figure 2.
Beyond that, it is clear that the housing market goes through long periods of either
steadily increasing or steadily decreasing home price inflation, and that these periods
have ended rather abruptly.
For example, it would appear that there was a major turning point in 2004 when
home price increases peaked, and that corresponds roughly to a time when the traffic of
home buyers also peaked, something that would probably stand out to home builders and
real estate agents as the true turning point. The plot of increases in home prices in Figure
2 looks rather more (abstracting from seasonality) like a broken straight line composed of
about three line segments. The rate of growth of home prices made abrupt changes in
trend in 1991 and 2004. Because of the suddenness of the change in growth rates, these
dates are only a couple years away from the breakpoints indicated by looking at the
Levels chart, 1989 and 2006, in Figure 1.
6
The conventional forecast from business economists at this writing in mid 2007
seems to be that the market for homes in the United States shows signs of improving, and
may have bottomed out. If that is the case then, judging from the perspective of Figure 2,
these economists are claiming to have identified another major turning point, when the
rate of increase of home prices will turn up again, after falling.
I want here to pursue the “why” of the apparent turning points, linking the
apparent regime changes to economic events and to principles of behavioral economics. I
will not be able to give a complete answer, as I will not be systematically pursuing all
economic factors that might logically have an impact on home prices. On the other hand,
in this paper I will concentrate on some apparently important psychological factors that
are likely to be omitted completely in a rigorous econometric analysis of the real estate
market.
The method here will be largely narrative, recounting the stories that people told
about the market of the times. Economists are usually very careful to avoid entering such
evidence. And yet, research by psychologists has found that narrative-based thinking is
extremely important in human decision making. People’s thinking is often more
influenced by human-interest stories than they are by quantitative evidence; see for
example Schank and Abelson (1977, 1995). Pursuing such an analysis may actually help
us to forecast turning points, providing information that is hard to pursue with rigorous
econometric analysis, or that may at least augment such econometric analysis by
suggesting alternative models or suggesting priors for models.
7
Supply and Demand, Investor Optimism, and Uniqueness Bias
At the risk of repeating the obvious, but for the purpose of making sure the reader
is online with some important facts, let us first reflect on those ubiquitous terms “supply”
and “demand” that determine, by their intersection, prices in any market. The price has to
clear the market continually. If there is an imbalance between supply and demand at any
time, the price will have to change immediately.
It would seem that demand for housing services should be relatively inelastic in
the short run, especially with regard to the number of units (rather than their size). Most
families want just one house. The decision to own two or more houses, or the decision to
break up the family to spread out over more houses, is not made very often—most
commonly only at important life turning points or job changes. It is difficult for builders
to transform two small housing units into one larger unit, or one large unit into two small
housing units, without great costs. Hence, even small changes in the number of housing
units might be expected to cause major short-run changes in home prices.
However, home prices do seem to show enormous momentum, and sudden
changes in the market seem rare. In a speculative market, a sudden change in some
component of supply or demand may produce little price change if people think that the
change is temporary, and so another component, a speculative component, offsets the
sudden change. But the speculative component is inherently psychological, potentially
unstable, and subject to contagion and herd behavior. People may change their mind
about whether a change in price is only temporary or is the beginning of a new trend.
They are especially likely to change their mind because we have professional marketers
8
whose job is to get some kind of social response moving, and, when they do find some
advertising pitch that resonates with investors, they will run it for all it is worth.
The supply of housing is dictated by the decisions of builders, who face markets
for construction labor, materials and land prices. According to the simple “Tobin’s Q”
model of investment, whenever home prices are high relative to construction costs,
construction will proceed at a relatively high rate, until the gap between home prices and
construction costs is closed off by new construction. If construction could be done
instantaneously, it would not matter whether prices are rising or falling, builders would
look only at the current price and build whenever price is high relative to construction
costs. Since there are lags on the order of a year between decision to build and
completion of a housing unit, builders will tend to pull back in a period of declining
prices even if prices are high relative to construction costs, but will continue to build at a
high rate if housing prices are still expected to be high by the time the construction can be
completed.
Analysis of past booms seems to indicate that investors in both the stock market
and the housing market seem often not to understand the supply response to price
increases. These are normal intelligent people, why would they repeatedly make the same
mistake again and again? There seems to be what I will call a uniqueness bias, a tendency
for investors to overestimate how unique an investment they favor is, failing to take
account of the inevitable supply response to high prices. The uniqueness bias is reflected
in quite a number of anomalies of human judgment that psychologists have documented,
including the “representativeness heuristic,” “overconfidence,” “wishful-thinking bias,”
“spotlight effect” and “self-esteem bias.” The uniqueness bias is related to failure to
9
imagine how many possible competitors there are, a tendency to think highly of oneself
and one’s associates and an association of investments with one’s sense of personal
identity with an identified business model.
The uniqueness bias has its effect in the stock market by encouraging people to
think that a company’s market position is unique, and thus underestimating how quickly
new competition will move in to close off any initial advantage. Gordon Philips and
Gerard Hoberg, in their 2007 study of booms in individual stocks, found that those in
competitive industries, not concentrated industries, show significant downturns following
high valuations. For competitive industries, stock returns are low following high industry
valuation and investment. They concluded that firms and the investors in these firms face
a signal extraction problem in booms, not knowing whether other firms have the same
apparent opportunities, not recognizing potential competition. They are thus vulnerable to
“new era” booms, overinvesting while neglecting to consider that many others are, or
soon will, be making essentially the same investments.
The uniqueness bias has its effect in the housing market when people imagine that
the city they live in is unusually attractive, and increasingly so. They fail to understand
that new such cities can be constructed in what are today cornfields or forests. In their
1990 paper, “The Baby Boom, The Baby Bust and the Housing Market,” N. Gregory
Mankiw and David Weil argued that the housing market would soon crash as the baby
boomers retired, neglecting to consider how supply would adjust to any such change in
demand. In their 2004 paper “Superstar Cities,” Joseph Gyourko, Christopher Mayer and
Todd Sinai argue for extrapolating some long-standing trends in major US cities, arguing
10
that these superstars will only grow in status, assuming implicitly that there can be no
new supply of the services those cities provide.
We have seen many examples of such thinking in the history of economic
thought. I wish to turn to some of these now, with special attention to the behavior of the
markets around what later proved to be major turning points.
Analysis of Ends of Booms
The ends of booms seem to be associated both with surprises at the increase in
supply of the underlying investment, and the negative effect of this increase in supply on
price. I will give some information about the ends of the stock market boom of the 1990s,
of the housing boom in Southern California in the 1880s, the end of the Florida land price
boom of the 1920s, and the end of the US home price boom of the 1980s, before
reflecting on the likely outlook after the recent boom in the housing market in the US.
The End in March 2000 of the Stock Market Boom of the 1990s
The end of the stock market boom of the 1990s, in 2000, coincides with the end of
a 1990s boom in corporate earnings. Both the real S&P 500 Index and the real S&P 500
earnings peaked in 2000. Real monthly average stock prices (as measured by the S&P
500) fell 47% between the peak in August 2000 and the trough February 2003. Real S&P
earnings fell 55% between the peak in September 2000 and the trough in March 2002. It
would appear that earnings explain the timing of the peak in the stock market. It is
certainly not quite right to conclude just this, however. In history, other major changes in
corporate earnings have not had such massive effects on the stock market: something was
11
different in the 1990s that caused such an intense market reaction to earnings changes.
And, of course, the earnings changes are not entirely exogenous to the stock market. The
falling stock market after 2000 helped bring on the recession of 2001, which helped bring
down earnings, and recessionary drops in earnings should not be reflected fully in stock
prices.
The end of the “Internet” or “Dot Com” boom, and the intense media response to
it, was part of the ambience at the end of the stock market boom of the 1990s. Jack
Willoughby wrote a story in Barron’s, the March 20, 2000 issue, entitled “Burning Up,”
that argued that many Internet companies were rapidly running out of cash. The article
included a ranking of Internet stocks by how many weeks left until they were out of
money. The story provoked an intense reaction. Willoughby told me that he was
astonished at the response he got to this article. It was an article put into a framework that
attracted public attention, at just the right time, and was talked about incessantly. The
story of a ranking of stocks by the number of weeks they had left had word-of-mouth
potential, and spread like wildfire.
Included in the story were examples of Internet companies underestimating the
competition, imagining that they were unique because they had a clever idea to exploit
the Internet, when in fact there were competitors waiting in the wings. Willoughby gave
one example of such a story in that article:
“A good example is eToys, a toy retailer that came public at $20 and surged to
well over $80 amid great public enthusiasm. The concept was easy to
understand and promised great riches. But the competition, in the form of Toys
R Us, did not roll over and play dead. Toys R Us launched its own Website, and
ardor cooled for eToys. Today shares of eToys repose at 113⁄4. All those people
who bought in at prices ranging from $20 to $80 are none too eager to buy more
shares, even at $12. EToys has enough cash on hand to last only 11 more
months, so stay tuned.”
12
The story of eToys became a whopping embarrassment to those who had invested in it,
for failing to see the elementary fact of supply response, of competition coming.
Indeed, an important factor that triggers the psychological end of a boom seems
often to be stories in the news media about people who have made stupid mistakes. Such
stories have word-of-mouth potential much more powerfully than stories about balance
sheets or technical indicators. Even though the stories are of rather extreme and perhaps
rare events, they are vivid stories that become connected in the public mind with the
entire boom, and serve to embarrass promoters and buyers alike.
In terms of expressed optimism about the course of the stock market, there is no
clear marker of the turning point. I have been compiling expectations data from both
individual and institutional investors since 1989, and these data are now being compiled
in the form of investor confidence indices under the auspices of the Yale School of
Management. The “One-Year Confidence Index” is the percent of investors who say, in
answer to one of the survey questions, that they think the stock market will go up in the
next year. After the peak of the stock market in early 2000, the percent expressing
confidence went up, rather than down. Of course, one might not expect to see drops in
confidence at a time of newly lowered prices. The natural interpretation of these results is
that prices fell until confidence that prices may rise was restored.
And yet the turning point in the stock market in 2000 does seem to be a time
when people who thought that the stock market boom was a result of human foolishness
were finally winning out and the jokes were starting to really hurt. In March of 2000, the
13
story of foolish investors was so strong that even the venerable Consumer Reports wrote
dismissively of them:
“Whatever happened to the old idea that patience and a steady long-term
perspective were the keys to investing success? They seem such quaint virtues
during these days of go-go Internet stocks, gaga equity mutual funds sporting
triple-digit returns, and up-to-the-minute bulletins on the Internet and Cable-TV
financial-news channels.”
The intensity of the public reaction to the stories of human foolishness was
augmented by a feeling that not only were people foolish, but also that in many cases
they had been duped, they had been had. The many stories of accounting irregularities
and fraud, leading to some heavily-covered trials of corporate executives, intensified
these feelings.
There is a factor that Bohnet et al. call “betrayal aversion,” which they argue is
actually stronger in many circumstances than pure risk aversion. People do not like to be
betrayed, to be victims of others’ schemes. Because of the intensity of this betrayal
aversion, the change in reaction is especially intense.
14
The end of the California Real Estate Boom of the 1880s
The California boom of the 1880s is very interesting to look at because it
happened so long ago, over a century ago, is almost completely forgotten, and yet it bears
striking similarities to the more recent real estate booms. There are no price index data
for the boom, but the boom was actively covered in newspaper accounts all over the US
at the time, accounts which are still available to allow us to trace out the course of the
boom. This boom, as in other booms since, seems to have the form of neglecting to
consider the supply response (new homes built) and the psychological market reaction.
The California boom, which took place in southern California, notably Los
Angeles, over much of the 1880s until a peak in 1887, came to an end in the relatively
mild recession of 1887-8. But, the dramatic collapse of real estate prices in California
after the boom must have another explanation than just the recession.
One can still today read the advertisements for real estate of the time, preserved in
old newspapers. In the early 1880s, the advertisements were relatively dignified and
straightforward. As the decade moved on, the advertisements became more focused on
the opportunities for rapid profits to be made in investing in southern California real
estate.
A December 1887 advertisement for lots for homes in the Los Angeles Times said:
“Phenomenal Success!” “Sales unprecedented in real estate records,” “This price will
positively be advanced in a very few days.” Another ad in the same months said “Prices
here will inevitably advance.”
15
Newspaper accounts of the foolishness of real estate investors in Southern
California began well before the peak of the boom. In January 1887 the New York Times
ran a story “Brisk Speculation in Old Los Angeles” which included the following:
“A gentleman with a roll in his hand enters a real estate office where an
Eastern man is seated, and is introduced as Dr. Blank. The doctor is asked if he
has sold that place of his? Yes, he has sold it. “I suppose you made something?”
“Well, yes, I sold at an advance of $4,000.” What was your original
investment?” “$1,500.” How long did you hold it?” “About three months, and I
have more to sell now,” and he unrolls a diagram of streets and lots. The
Eastern man is impressed by the remarkable advance—266 2/3 per centum in
three months—and so well authenticated. One of the brokers than narrates his
own conversion to belief in the firm foundation for present prices. Since 1881
the advance had been steady, with no reactions. Nobody complained of business
except those who had sold before the last rapid advance, and were,
metaphorically, ‘kicking’ themselves for not holding longer.”
As the boom unwound, in 1888 and 1889, newspaper articles became more
insistent that the boom was a fiasco. Newspaper ads for real estate became more
defensive, reacting to criticisms that lots had been sold for high prices in places that
would not plausibly be developed in the foreseeable future. A May 1888 real estate ad
included a map of the city around the site, asked the reader to study the map, and said
You will find it to be CLOSE INSIDE, with all the convenience and advantages of living
in the city.” Another real estate ad in the Los Angeles Times in June 1888 said “Wildomar
lots and lands are not a venture, but an investment in a well watered, well improved,
thriving town, never boomed and never slumped.
As time went on, the number of ads for real estate in the Los Angeles Times fell
dramatically, to be replaced by advertisements for bicycles, cigars, and other consumer
items.
16
The End of the Florida Land Boom of the 1920s
It seems that the next major real estate boom in the United States, that achieved national
attention, was the Florida land boom of the 1920s. Stories of appreciating properties in
Florida began after the recession of 1920-1. Stories in early 1920s of people striking it
rich in Florida land boom steamed right through 1923-4 recession. Newspaper articles
started to appear frequently in early 1925, exposing schemes and doubting prices. The
end of the boom has been attributed to the disastrous Florida hurricane, Sept 1926, and
recession October 1926-September 1927. But, it seems, the true end of the boom is
marked as well by indications of oversupply of new homes and of changes in investor
psychology. In October 1925 the Chicago Tribune wrote:
“On the other hand, there are developments along the Dixie that will never be
developed—sheer frauds. We’ve seen some of them, driving along the highway.
One sometimes passes a pair of concrete pillars—the city’s gateway—and a lot
of street posts stuck up along the pines, ten miles from anywhere, maybe in the
heart of a turpentine grove with nothing in sight to warrant their ever being
developed. The lots, however, have probably been sold, for the professional
sharper can always land the suckers, whether it be in oil wells, bucketshops,
silver mines, gold bricks, or the little pea and the three shells.”
The stories of a potentially endless supply of new lots, and of people being duped by
operators had the same word-of-mouth resonance in the 1920s that it had in the 1880s,
and once again the volume of real estate ads tapered off with the end of the boom.
The End of the U.S. Real Estate Boom of the 1980s
The late 1980s U.S. home buyers showed a high level of excitement and
optimism. Karl Case and I collected homeowners’ expectations in 1988 for future home
price changes in their city. We ask the question “On the average over the next ten years
how much do you expect the value of your property to change each year?” Expected
17
price increases were very high, particularly in the boom cities. The surveys found also
evidence of a high level of social contagion.
Residential investment rose during this boom, to a peak of 4.9% of GDP in 1987,
and the new supply must have contributed to the end of the boom. The drop in home
prices brought about a sharp decline in residential investment too, down to 3.4% of GDP
in 1991.
The 1980s boom came early on the east coast than on the west coast. According to
the Case-Shiller indices, Boston home price increases peaked in late 1985, while Los
Angeles home price increases peaked in late 1988. However, the time of maximum price
drops occurred simultaneously in the two cities, in the period ending early 1991.
As can be seen in Figure the end of the real estate boom of the 1980s was quite
sudden, with a brief period of sharply falling prices in late 1990 and early 1991. The
period of sharpest declines in home prices corresponds almost exactly to the Persian Gulf
War. The threat of war began in mid 1990 when Saddam Hussein built up his troops near
the border with Kuwait, suddenly occupied Kuwait, and challenged the US to respond,
threatening dire consequences if it did. Saddam’s men were reportedly dug in in Kuwait
and had heavily mined the areas US troops would have to traverse, and so there was great
concern about the possibility of heavy US losses. Saddam was also thought likely to
launch terrorist actions against Americans around the world. The actual war began with
the US invasion of Kuwait August 2, 1990. The war ended February 28, 1991. The
biggest 12-month drop in home prices shown in the figure occurred in the 12-month
period ending January 1991, with real prices falling a total of 12.9%. Traffic of
prospective home buyers tumbled exactly parallel to this event. It is possible that the
18
psychology of this war, with the prospect of sharply affecting our daily lives, had an
impact on people’s mood for shopping and buying a new home.
The stock market confidence indices that I have been creating since 1989, now
under the auspices of the Yale School of Management, show a sharp drop in “Buy-on-
Dips Confidence” among institutional investors in late 1990 and early 1991, and recovery
in late 1991. Buy-on-Dips Confidence is measured by asking respondents whether they
think that sudden drops in the stock market are likely to be quickly reversed. The stock
market (as measured by the Standard & Poor’s 500 Index) fell 15% between June and
October 0f 1990, and began a sharp recovery in February 1991, just as the Gulf War
ended. Thus, it might appear that the Gulf War indeed had an effect on confidence.
There was also a U.S. recession, which began, according to the NBER dating, in
the third quarter of 1990 and ended in the first quarter of 1991. It is hard to tell, of course,
which event was the more important in producing home price decreases. The recession
and the Gulf War were also not unrelated. The same effects that would bring on whatever
psychology produced a sharp decline in the traffic of prospective home buyers would
seem likely to contribute to a recession as well.
Other countries who were involved in the Gulf War with the US included Canada,
the United Kingdom, and Australia. All of these countries saw drops in home prices at
the time of the Gulf War. The sharpest drops in Canada were seen at almost the same
time as in the U.S. The sharpest drop in UK home prices was a bit later, in late 1991 and
1992. The drops in Sydney home prices began in late 1989 and ended at the end of 1990.
19
The Real Estate Boom of the 2000s
The same factors that appeared to have been at work in the real estate booms discussed
above appear to have been at work in the 2000s boom, a boom that shows signs of
perhaps being near its end now in 2007. The same social contagion appears to have been
at work. We have seen the same burgeoning of real estate advertisements.
So, has anything we have learned by studying the ends of real estate booms let us
see more clearly whether the boom of the 2000s may be at end? Or, on the other hand, is
there any evidence that we may be near a different kind of turning point, like that in
1990-91, when the period of declining growth rates of home prices abruptly ended?
We have seen during this boom a burgeoning of real estate investment.
Residential investment as a share of GDP rose to 6.2% of GDP by early 2006, the highest
level since 1950. Casual observation suggests that the “uniqueness bias” seems still to be
at work. Most people do not seem to know that the supply of new homes is increasing so
fast, and when I have talked to people at such times they instead seem to be focusing on
stories of what will set their city apart. The phase of sense of betrayal and embarrassment
for participating in the boom does not appear to have set in, at least yet.
The boom of the 2000s appears to be much bigger than any that preceded it. The
California real estate boom of the 1880s, and the Florida boom of the 1920s, were the talk
of the nation, but those booms did not materially spread to the rest of the nation. It seems
that the 2000s boom has a different story behind it that allows broader contagion. The
California boom of the 1880s and the Florida boom of the 1920s appear to have been
driven, at least in part, by the story that people were then just discovering the beautiful
climate of these exotic places. The story of the boom of the 2000s seems instead to have
20
been one of a growing world economy, producing greater affluence, a rising tide of new
capitalists who may outbid ordinary people, who could be forever unable to afford a
home. That story invites a boom that spreads everywhere—at least to any place in the
world where there is a sense of uniqueness and not of abundance of undeveloped land.
In the 2000s, we have much more data to observe the real estate market, and at a
time when there are lots of fears that the market may be slowing, we have people
dissecting the data to look for clues on the future course. Unfortunately, much of the data
have not been produced long, and we certainly do not have the data to allow us to
compare it with the major real estate booms of the past.
The Michigan Consumer Sentiment Survey has for decades been including a
question asking respondents whether they think it is a good time to buy a house. It would
seem that answers to this question would inform us about investor optimism in the
housing market. Unfortunately, judging from the answers to their follow-up question,
which ask for factors that underlie their answer, it seems that most people think of
interest rates when posed this question, not about changing expected rates of appreciation
in the housing market. A good time to buy for them is a time when mortgage rates are
low. The changes in interest rates are salient facts that come to their mind when asked
whether this is a good time to buy, rather than changes in their expected appreciations of
home prices which are not quantified in any publication that they can see.
Karl Case and I have updated our 1988 questionnaire survey results of recent
homebuyers. We now have answers to this question for the years 2003, 2004, 2005, and
2006 as well. These data are consistent with a peak in expectations for home price
increases around 2005, and signs of reduced expectations for price increase in 2006.
21
The most significant development in terms of new data on housing are the new
futures markets for homes, begun in May 2006 by the Chicago Mercantile Exchange in
connection with the firm MacroMarkets LLC, that I, along with Allan Weiss and Samuel
Masucci, co-founded. Although this marketplace was preceded by spread betting firms in
the United Kingdom, this is the world’s first successful true futures market for home
prices (following an unsuccessful attempt in 1991 at the London Futures and Options
Exchange). Ten cities are traded, along with the composite index that appears in Figure 1.
The contract mature on the last Tuesday of February, May, August and November, and
are cash settled at 250 times the latest announced three-month-moving index, and the
latest index is based on data available for home sales two months earlier, because of data
reporting lags. The longest horizon futures contract at present is one year, but because of
data lags, one could say that the longest horizon is really about eight months. Each index
is based at 2000=100. Since the price of homes has more than doubled since 2000, the
Standard & Poor Composite Home Price Index is over 200 and so the notional value of
one contract is over $50,000. As of January 2007, the latest value of the index (for the
three months ending November 2006) was 223.58 and so the notional value was $55,895.
Total open interest in all eleven futures contracts was $91.6 million.
The volume of trade in these markets has been small, but it appears to be growing
now. Almost from the beginning of trading in these markets they have been predicting
substantial declines in home prices over the succeeding year.
There are other indicators that suggest that there have been recent improvements
in the US housing market. The number of US housing permits issued has dropped sharply
in 2006 and 2007, but has increased, ever so slightly, in February 2007. The NAHB
22
Traffic of Prospective Buyers Index, which we saw in Figure 2, peaked in 2005, but
while it is still low, has shown substantial improvement since. Prices of stocks of major
home builders, which peaked in late 2005 and then fell sharply, have been going up since
early 2006.
There are also examples of real estate markets in other countries that showed
signs of falling prices, but have recovered since. In London, England, home prices,
reversing a five percent home price decline from early 2004 to early 2005, have been
rising since. Home prices in Sydney, Australia, started falling in 2003, and while they are
still falling in that city, the rate of decline has tapered off, and there are sharply rising
prices in Perth and other Australian cities.
Should we then infer that there is a possibility that the boom psychology of the
2000s housing boom in the United States will come back to revive the boom? There
certainly is a possibility of that. No one seems to understand the social psychological
processes that produce boom psychology.
But, one must remember that the high ratios of home prices relative to
fundamentals, notably construction costs, have produced a supply response in housing
that seems to be surprising people as it has always done in prior booms. There seems to
be a chance that the element of surprise will turn into a downturn. We are already seeing
this happen, with prices falling in most major cities in the United States.
Summarizing – What Marks Turning Points in Housing Booms?
We have looked at several different ends of booms—the end of the stock market
boom of the 1990s, the end of the California real estate boom of the 1880s, the end of the
23
Florida land boom of the 1920s, the end of the national real estate boom of the 1980s, and
the recent end (perhaps) of the national real estate boom of the 2000s. What seems to
account for their abrupt ends?
We have looked at accounts of these events, at the narrative histories of these
events. These accounts do not seem to show clear reasons for the somewhat abrupt ends
of booms. In one case, the Florida land boom of the 1920s, an exogenous event, a
hurricane appears to have played a role in the collapse after the boom. In another case
again an exogenous event, the Persian Gulf War, appears likely to have been at least part
of the reason for the abrupt change. In others, no exogenous event clearly marks the
turning point. The causes of the turning point remain fuzzy.
And yet the change in reporting of these booms does indicate that a psychological
element to these booms did matter. There were in some cases indications that people
were “wising up” to abuse and betrayal of some who had exploited them during the
boom, and it was starting to become embarrassing to admit that one was caught up in the
boom.
These narrative accounts do not prove anything, and we do not know that the
change in thinking that appears to accompany ends of booms was in any sense the cause
of the end of the boom. The change in thinking cannot be measured accurately, as we
have only media accounts that suggest at it, that represent some journalists’ impressions
that may not be replicable. Some economists would therefore be inclined to exclude any
such effects from the economic model of the boom, and to try to explain the change in
terms of some more well-measured economic effects.
24
But, if one considers that the prices paid for houses, as for any other speculative
investments, surely reflects people’s willingness to pay, then the change in attitudes must
have had an impact on prices. Just because we cannot precisely quantify and prove such
an effect does not mean we should revert back to a null hypothesis that the changing
psychology has no effect on home prices.
The best guess is that ends of housing booms have multiple causes, and cannot
generally be interpreted as just an unraveling of boom psychology. Still a rising sense of
enthusiasm and excitement for the investments, followed by a sense of betrayal and
embarrassment at having fallen for the boom and underestimating the supply response to
the boom, played a significant, if unquantifiable, role in the booms and their subsequent
break.
25
Home Price Index 250 1600
Jun-06
Mar-00
1400
200
1200
Stock Prices 1000
150
800
Oct-02
Oct-89
100 600
Home Prices
400
Stock Price Index
50
200
0 0
February August 1987 January July 1998 January July 2009
1982 1993 2004
Figure 1. Prices in stock market and housing market, with dates of major turning
points indicated. Standard and Poor 500 Stock Price Index (moving average of
daily closing values for the three months ending with the month indicated,
January 1987-March 2007) and Standard and Poor Composite Home Price Index,
which is based on ten major U.S. cities, monthly, January 1987 to December
2006.
26
NAHB Traffic Index
70 30%
Traffic of Prospective Home Buyers
25%
60
20%
50
15%
40 10%
5%
30
0%
20
-5%
Annualized Monthly Increase of Home Prices (%)
Home Price Increase
10
-10%
0 -15%
1980 1985 1990 1995 2000 2005 2010
Figure 2. Month-to-month change in the Standard & Poor’s/Case-Shiller Home
Price Index, monthly, January 1987 to November 2006 along with National
Association of Home Builders Housing Market Index, Traffic of Prospective
Home Buyers, monthly January 1987 to January 2007.
27
References
Bohnet, Iris, Fiona Greig, Benedikt Herrmann and Richard Zeckhauser “Betrayal
Aversion,” unpublished paper, Harvard University, 2007.
Case, Karl E., and Robert J. Shiller, "The Behavior of Home Buyers in Boom and Post-
Boom Markets," with K.E. Case, New England Economic Review, Nov./Dec.,
1988, pp. 29-46.
Case, Karl E., and Robert J. Shiller, Home Buyers Survey Results 1988-2006,
unpublished paper, Yale University, 2006.
Gyourko, Joseph, Christopher Mayer and Todd Sinai, “Superstar Cities,” unpublished
paper, Wharton School, 2004.
Phillips, Gordon and Gerard Hoberg, “Real and Financial Industry Booms and Busts,”
unpublished paper, University of Maryland, 2007.
Mankiw, N. Gregory, and David N. Weil, “The Baby Boom, the Baby Bust, and the
Housing Market,” Regional Science and Urban Economics 21(4):541-52, 1992,
NBER Working Paper No. 2794, 1990.
Schank, Roger C., and Robert P. Abelson, “Knowledge and Memory: The Real Story,” in
Robert S. Wyer, Jr., editor, Knowledge and Memory: The Real Story, Hillsdale
NJ: Lawrence Erlbaum Associates, pp. 1-85, 1995.
Schank, Roger C., and Robert P. Abelson, Scripts, Plans, Goals and Understanding, John
Wiley & Sons, 1977.
Shiller, Robert J., Irrational Exuberance, 2nd Edition, Princeton University Press, 2005.
28