1. home and garden

IRES2013-021
IRES Working Paper Series
Why do buyers pay more than what the house
is worth? A Case Study of Singapore’s Housing
Market
Nai Jia Lee &
Qian Yi Yeo
October 2013
Why do buyers pay more than what the house is worth? A Case Study of Singapore’s
Housing Market
By Lee Nai Jia*
and
Yeo Qian Yi
National University of Singapore
School of Design and Environment
Department of Real Estate
Abstract
Managing market sentiments in the residential property market is a constant challenge
faced by policy makers around the world. This is especially so the Governments identify the
signs of overheating. Using Singapore as a case study, this paper attempts to identify the
factors that motivate homebuyers to pay prices higher than valuations. We consider that both
buyers and sellers know the valuation of a flat prior to the price negotiation. The secondary
market for public housing in Singapore serves as a good laboratory for us to test these factors,
as Singapore’s institutional framework helps to circumvent several endogeneity problems that
have plagued past empirical studies. Our empirical results show that buyers tend to pay
higher premiums over valuations when the subject properties are located near transport nodes
and amenities. In addition, buyers tend to pay above valuations when the market is booming.
These results support the hypothesis that valuation reports hardly dampen the investment
value perceived by buyers and sellers. They also suggest that any disclosure of market
information and improvement of market transparency is unlikely to affect how individuals
perceive the real estate market.
* Corresponding author.
Address: National University of Singapore, Department of Real Estate, 4 Architecture
Drive, Singapore 117566.
1
1
Introduction
The housing market is characterised by booms and busts. In the United States, the last
observed boom started in 1997 and peaked in 2006 (see Figure 1). In 2007, the widespread
default of subprime mortgages triggered a series of chain reactions that caused the biggest
recession in the United States since the Great Depression. Asia’s housing market experienced
a similar series of booms and busts. In 1997, several emerging and developed economies in
Asia, such as that of Singapore, suffered one of the worst financial crises caused by a sudden
movement of capital flows. The same countries have experienced a housing boom since
2008. China’s rapid economic development has spurred the region’s economic growth, and
Singapore has become a key trading partner in the Asian region for imports and exports.
Asia’s economic growth has initiated the recovery of the property market. This sudden turn
of events is depicted in Figure 2, which describes the price movements of Singapore’s private
housing market. These price cycles pose political challenges for the government. At the
upturn of the price cycle, the increase in housing wealth is accompanied by higher
consumption of non-housing goods and borrowing of larger loans. The government needs to
manage the inflationary pressure and ensure the viability of the financial system due to the
increased borrowing. When the housing market is not doing well, the government needs to
refinance the bad loans. In addition, they need to manage their spending with less public
funds from property tax. Figure 1: US Case-Shiller Repeat Sales Index
250
200
150
100
Index
50
Dec 31, 1890
Dec 31, 1896
Dec 31, 1902
Dec 31, 1908
Dec 31, 1914
Dec 31, 1920
Dec 31, 1926
Dec 31, 1932
Dec 31, 1938
Dec 31, 1944
Dec 31, 1950
Dec 31, 1956
Dec 31, 1962
Dec 31, 1968
Dec 31, 1974
Dec 31, 1980
Dec 31, 1986
Dec 31, 1992
Dec 31, 1998
Dec 31, 2004
Dec 31, 2010
0
Source: Case Shiller Price Index and Standard & Poor
2
Figure 2: Singapore Private Property Price Index (1990-2013)
Source: URA REALIS
Researchers have argued that one of the key causes of booms and busts in the housing
market is the inability of buyers and sellers to estimate house prices correctly. It is not
surprising that homebuyers overpay for properties, as the housing market is hindered by high
transaction and information costs. What is puzzling is that this occurs despite both buyers and
sellers knowing the valuations of houses before they complete the transactions. Valuations
are performed and provided by independent appraisers to determine the mortgage amounts
that the buyers can borrow. According to the Royal Institution of Chartered Surveyors, the
market value is defined as follows:
“The estimated amount for which an asset or liability should exchange on the valuation
date between a willing buyer and a willing seller in an arm’s length transaction after proper
marketing and where the parties had each acted knowledgeably, prudently and without
compulsion.”
3
Given that buyers and sellers acknowledge an appraiser’s value of a subject property,
we expect the difference between price and valuation to be random after controlling for every
other factor. The chance of meeting a generous prospective buyer or a hard-bargaining seller
comes down to pure luck. However, anecdotal evidence suggests that the difference between
price and valuation in the secondary public housing market has remained consistently
positive since 2007. In a booming market, it may seem intuitive that high demand and low
supply allow sellers to charge high cash-over-valuations. However, if the classical
assumptions of buyers and sellers are not violated, valuations should reflect the market
conditions with a high market value. Any excess cash premium is probably caused by a
temporary exogenous shock, and a price should be equal to the valuation at equilibrium. We
should similarly not expect properties to be sold consistently below valuation in a down
market, as valuations should reflect such a market’s declining conditions.
We attempt to solve this puzzle by examining the revelation of a valuation on a final
transacted price, and attempt to discover why buyers and sellers agree on prices that are
persistently higher than valuations. We suspect that behavioural theories can explain why
buyers pay high cash premiums over valuations. First, it is likely that persistent high cashover-valuations result from buyers’ irrational expectations of future price appreciations that
are further capitalised into an agreed-upon price. In a footnote, Seiler et al. (2013) suggest
that valuation does not equate to price. Although a price reflects the perceived capital
appreciation of both the buyers and sellers, a valuation reflects only the current market value.
In a similar paper to ours, Neo et al. (2008) argue that homebuyers set their reference values
based on the location of a property and make insufficient adjustments to the final price,
leading them to overpay on properties. In our paper, we have a cleaner test that examines how
the ‘anchoring process’ is applied to valuations to determine a final transacted price.
Second, we suspect that the amount of an agreed-upon cash-over-valuation may be
influenced by how market agents code, categorise and evaluate events. A cash-over-valuation
may simply reflect the transaction utility of a house or its perceived value. In other words,
buyers are willing to pay high cash premiums when they believe they are receiving a good
deal on purchasing a unit that is close to certain amenities. Meanwhile, such amenities are
accounted for in valuations, and the excess premium payable is hard to justify. According to
classical economic assumptions, the way buyers and sellers conduct their mental accounting
4
is likely to lead them to pay more excess premiums for bigger flats compared with rational
agents.
Finally, the revelation of a valuation may provide information that influences the
bargaining position of a homeowner. A property with a high reported value may suggest its
scarcity and thus strengthen the bargaining position of the seller, who would be able to justify
the high valuation for a higher premium. In contrast, the valuation may not influence the
reference value that the sellers and buyers have in mind. Instead, the buyers and sellers may
believe that the valuation does not sufficiently capture the amenities cash premium required
to achieve their reference values.
To test our hypotheses, we use a comprehensive database of housing market
transactions conducted in Singapore’s public housing market from 1998 to 2013. Focusing on
Singapore’s public housing market helps us overcome some of the empirical problems that
have been inherent in the literature. For instance, the study by Neo et al. does not reflect the
excess premium caused by anchoring effects, as location proxies may reflect the unique
features of units located in the prime districts (Haurin et al. 2010). The prime districts have
fewer public housing precincts than the other districts, and the amenities that are close to the
houses in the prime districts are unique.
In addition, empirical studies on the behavioural patterns of homebuyers and sellers
rely on transactions involving houses with different designs and attributes. Using the hedonic
regression method to uncover such attributes without a large transaction database may lead to
an omitted variables bias (Watkins 1999). However, having a large database may create
technical problems in forming the valuations. Using the algorithm created to find the
valuation of each transacted property in the database would be too time-consuming. The
public housing market transaction database helps us circumvent these technical problems. It
contains over 100,000 transactions that allow us to use the hedonic regression design.
Further, most of the units are similar in design, and the public housing precincts have similar
amenities and facilities. The differences between the valuations derived from the valuation
algorithms determined by the literature are nominal. Finally, the regulations governing the
public market help decrease the need to identify proxies for information asymmetry.
5
In addition to having a better dataset with which to empirically examine our
hypotheses, this paper makes several contributions to the literature. First, it extends how
valuation disclosure affects the cash premiums paid to sellers. Previous studies have focused
on how agents form myopic expectations of house price returns (Case and Shiller 1994). To
the best of our knowledge, no study has explored whether the disclosure of agreed-upon
premiums in a district affect the formation of house price returns. Second, it expands our
understanding of why market agents agree on final prices and excess cash premiums. Our
results show that the cash-over-valuations agreed upon by buyers and sellers tend to be sticky
over time, providing support for the anchoring hypothesis. Furthermore, it finds that the cashover-valuations for bigger types of units are higher than those for smaller units. Cash
premiums also increase when the units are closer to amenities and transportation hubs. This
further suggests that the context in which purchase decisions are framed affects the final
payable premium amounts.
The remainder of this paper is structured as follows. We begin by providing an
overview of the public housing market in Singapore. We then review the literature on buyer
behaviour in the housing market and other asset markets to provide a context for our tested
hypotheses. We present our data and discuss empirical specifications and methodological
issues. Finally, we present our results and discuss their implications.
6
2
Singapore’s Housing Market and its Stylised Facts
The division of Singapore’s housing market into public and private housing sectors is its
defining characteristic. The public housing sector, which is closely monitored and regulated
by the housing authorities, supplants the role of the residential rental market seen in other
countries. It provides housing for low- to middle-income homeowners, and helps them move
up the housing ladder. First-time homebuyers can purchase new flats at subsidised rates and
pay the down payments with their social security savings and cash. In addition, the
government ensures that most low- or middle-income homeowners can afford new housing
by setting low housing prices and low interest rates on public loans. Individuals who cannot
afford the cheapest public housing available can apply for state-offered rental housing, where
the rent is made affordable for the lowest income. Large subsidies are offered to buyers of
public housing, and restrictions are imposed to prevent rent-seeking behaviour. For instance,
individuals who cash out subsidies by selling their public housing are required to pay back a
levy of 20% of the house price before they can purchase a new public housing unit from the
housing authorities. First-time homebuyers cannot own a property, and their total household
income cannot exceed the income ceiling. The private housing market also caters to the
demand for upper-middle- to high-income households. The government does not cast
restrictions on buyers other than in cases of foreign ownership of landed properties in
Singapore. That said, the government intervenes in the private market through zoning
ordinances, property taxes, government land sales programmes, stamp duties and lending
regulations.
Unlike in most countries, there is a secondary market for public housing in Singapore.
The function of the secondary public housing market is similar to that of the private housing
market, except that both buyers and sellers are required to fulfil several eligibility conditions
before transactions can take place. Sellers must satisfy a minimum occupation period 1 before
they can sell. In addition, they can sell to other buyers of other races only if the racial quota
for the precinct in which the subject property is located has not been reached. Although
foreigners are not allowed to purchase public housing in the secondary market, permanent
1
The minimum occupation period changes over time depending on the state of the market. Before 2011, the
minimum occupation period for households using subsidies to purchase resale units was 2.5 years. Households
that used their equity and housing loans from private banks to purchase the resale units were permitted to sell
the properties in the secondary market after fulfilling the 1-year minimum occupation requirement. In 2011, to
curb speculation, the government imposed a minimum 5-year occupation period for any unit purchased after
2011.
7
residents of Singapore can purchase it if the proportion of permanent residents on the block
does not exceed 5% of the total residents. Singaporeans can apply for subsidies and public
housing loans for flats purchased in the secondary market. However, they must fulfil the
same conditions in the primary market to qualify.
The prices for public housing in the secondary market have gone through several
cycles (see Figure 3). The market crashed in 1997 due to the Asian Financial Crisis, and did
not pick up until 2006. The latest housing boom, which started in 2006 and continues today,
is driven by high economic performance and low interest rates. The prices in the public
housing market clearly mirror those in the private housing market, suggesting a close
relationship between both markets. Ong and Sing (2002) find that socially upgrading
households from the public market to the private market helps to link the markets. However,
they do not find any evidence that private homeowners cash out their private housing equity
and move down to public housing. Their story is consistent with the Singapore Census, which
has reported that less than 6% of private home dwellers downgrade to public housing.
Figure 3: Resale Price Index of HDB Resale Flats
Source: Housing and Development Board, 2012
During the boom in 2006, many buyers in the secondary public housing market had to
pay sellers cash-over-valuations to purchase flats. A cash-over-valuation is a cash payment
8
made to the seller in addition to the down payment required to purchase the flat. We refer to
the Housing Development Board (HDB) flat transaction process, depicted in Figure 4, to
further explain cash-over-valuations. When sellers decide to sell their houses, they or their
agents engage appraisers to value the properties. A valuation report is issued to determine a
mortgage, and the loan amount a buyer can take out depends on the valuation. The valuation
report is furnished to prospective buyers who come to view the house for sale. After visiting
the house, the prospective buyer negotiates with the sellers on the price of the house. The
difference between the agreed-upon house price and the valuation is the cash-over-valuation
that the buyer agrees to pay, conditional on the agreed-upon house price being higher than the
valuation. Once a prospective buyer agrees on a price with a seller, the buyer pays an option
fee that cannot exceed $1,000. The option-to-purchase period lasts 14 calendar days and ends
at 4 pm. The prospective buyer pays a fee to exercise the option. The option fee and the
option exercise fee cannot exceed $5,000 and must be more than $0. The buyer and seller
then arrange two appointments with the HDB to set up the property transfer.
Figure 4: Transaction Process of an HDB Flat
Step 1
• The seller engages an agent and lists the property on the public housing resale market.
• The agent seeks a valuation report for the property.
• The listing may not include the valuation.
Step 2
• The prospective buyer visits the subject property for sale and acknowledges the valuation
report.
• The prospective buyer negotiates with the seller on the final transaction price.
Step 3
• The prospective buyer pays the option fee. The buyer has the opportunity to refuse the option
if desired. The seller cannot negotiate with other prospective buyers at this point.
• The buyer executes the option by paying the deposit.
• The buyer and seller arrange two appointments with the HDB to complete the transfer.
The HDB first published the median cash-over-valuation in 2007 on its website due to
the media attention surrounding the high cash-over-valuations payable and the push towards
greater transparency. Figures 5 and 6 show a significant increase in the cash-over-valuations
9
payable from the second quarter of 2007 to the first quarter of 2013 for every housing type
and area. For instance, the median cash-over-valuation for a three-room HDB flat located in a
town near the CBD is about $22,500 (Kallang Whampoa) in 2007. By 2013, the median cashover-valuation rises to $53,000 for the same type of flat in the same town. Examining the
cash-over-valuations within each quarter, we can see that the median cash-over-valuation
tends to be low in the areas that are far from the central districts.
Figure 5: Median Cash-Over-Valuations for Resale Cases Registered in the First Quarter of
2013
Town
OneRoom
TwoRoom
ThreeRoom
FourRoom
Five-
Executive
Room
Ang Mo Kio
-
*
$30,000
$40,000
$50,000
*
Bedok
-
*
$30,000
$36,400
$45,000
*
Bishan
-
-
*
$50,000
*
*
Bukit Batok
-
-
$25,000
$34,000
*
*
Bukit Merah
*
*
$30,000
$50,000
$69,000
-
Bukit Panjang
-
-
*
$25,000
$35,000
*
Bukit Timah
-
-
*
*
*
*
Central
-
*
*
*
-
-
Choa Chu Kang
-
-
*
$26,900
$25,500
*
Clementi
-
-
$27,500
*
*
*
Geylang
-
*
$26,000
$41,000
*
*
Hougang
-
-
$25,000
$32,000
$40,000
*
Jurong East
-
-
$25,000
$32,000
$47,500
*
Jurong West
-
*
$25,000
$27,000
$33,000
$36,000
Kallang/Whamp
-
*
$30,000
$53,000
*
*
Marine Parade
-
-
*
*
*
-
Pasir Ris
-
-
*
$35,000
$40,000
$59,500
oa
10
Punggol
-
-
-
$43,000
$32,500
*
Queenstown
-
*
$27,000
$69,000
*
-
Sembawang
-
-
-
$28,000
$33,000
$40,000
Sengkang
-
*
*
$38,500
$35,000
$39,000
Serangoon
-
*
*
$44,000
*
*
Tampines
-
-
$27,500
$35,000
$43,000
$67,500
Toa Payoh
-
*
$32,000
$55,000
$75,000
*
Woodlands
-
*
$27,000
$30,000
$33,900
$57,000
Yishun
-
-
$26,000
$30,900
$38,000
*
Source: HDB
Figure 6: Median Cash-Over-Valuations for Resale Cases Registered in the Second Quarter
of 2007
Town
OneRoom
TwoRoom
ThreeRoom
Four-
Five-
Room
Room
Executive
Ang Mo Kio
-
*
$5,000
$15,000
$25,000
*
Bedok
-
*
$7,000
$7,000
$12,800
$28,000
Bishan
-
-
$15,000
$19,300
$28,500
$28,800
Bukit Batok
-
-
$5,000
$9,000
$16,500
$12,000
Bukit Merah
*
*
$15,900
$25,000
$40,000
-
Bukit Panjang
-
-
$2,000
$6,000
$0
$2,500
Bukit Timah
-
-
*
*
*
*
Central
-
*
$18,500
*
*
-
Choa Chu Kang
-
-
*
$5,000
$5,000
$6,500
Clementi
-
-
$7,300
$18,200
*
*
Geylang
-
*
$8,000
$15,000
$0
*
Hougang
-
-
$9,000
$9,000
$15,000
$11,000
Jurong East
-
-
$7,000
$10,000
$10,000
$20,000
Jurong West
-
-
$5,000
$7,000
$6,000
$0
11
Kallang/Whamp
-
*
$8,000
$22,500
$36,500
*
Marine Parade
-
-
$20,500
*
*
-
Pasir Ris
-
-
*
$5,000
$6,000
$10,000
Punggol
-
-
-
$3,000
$5,000
*
Queenstown
-
*
$14,000
$35,500
$42,000
*
Sembawang
-
-
-
$8,000
$6,000
$0
Sengkang
-
-
-
$6,000
$5,000
$0
Serangoon
-
*
$9,000
$12,000
$11,500
$10,000
Tampines
-
-
$7,000
$8,000
$10,000
$15,400
Toa Payoh
-
*
$13,000
$22,000
$34,500
*
Woodlands
-
*
$3,000
$4,000
$0
$0
Yishun
-
-
$5,500
$5,000
$2,000
$1,400
oa
Source: HDB
Cash-over-valuations have generated much interest in understanding the rationale
behind high housing costs. No study has thoroughly examined the causes of cash-overvaluations. The structure of the secondary public housing market allows us to better test the
behavioural economic theories. The market’s restrictions ensure a stronger consumption
motive among its buyers compared with buyers in the private housing market. Its citizenship
requirements remove the possibility of information asymmetry among buyers, which further
helps us isolate the anchoring and framing influences. More importantly, the homogeneity of
public housing and the similarity of public housing estate plans allow us to overcome the
possible biases that prevent the hedonic regression method from determining a valuation.
12
3
Literature Review
The housing and behavioural economics literature offers several possible theories that
explain high cash-over-valuations. First, less-informed buyers are likely to overpay for a
property compared with informed buyers (Miller et al. 1998, Lambson 2004, Watkins 1998,
Turnbull and Sirmans 1993, Myer et al. 1992, Neo et al. 2008). Such an information
asymmetry is created by buyers who are inexperienced in purchasing housing or who lack
sufficient knowledge of a neighbourhood’s institutions and attributes. Hence, an influx of
migrants and foreign investors cause prices to rise above valuations. Many studies test this
hypothesis using the hedonic regression method to examine whether the prices paid by locals
and foreigners for similar properties during the same period differ. In a recent empirical
paper, Neo et al. (2008) compare the property prices paid by foreigners and locals to test the
information asymmetry in Singapore, and find strong evidence that high excess cash
premiums are attributable to the buyers’ unfamiliarity with the market. The empirical results
of studies that examine the real estate market in the United States have been mixed. Miller et
al. (1998) compare the prices paid by Japanese and local buyers for properties in Honolulu
and find evidence supporting the information asymmetry theory. Lambson et al. (2004)
compare the prices paid by out-of-state and local buyers for similar properties, and also find
empirical support for the theory. However, Turnbull and Sirmans (1993), Watkins (1998) and
Myer et al. (1992) find no sufficient variation in prices to validate the theory.
Second, the theories presented in the time-on-market literature suggest that the
premiums reflect buyer urgency. High cash-over-valuations probably result from home
sellers being more patient than buyers and accepting longer time-on-market periods for their
properties (Arnold 1999, Anglin, Rutherford et al. 2003). To initiate a deal, buyers must pay
excess cash premiums to the sellers.
We are able to rule out the two preceding theories as possible explanations for the
high cash-over-valuations observed in Singapore’s public housing market. Information
asymmetry is not likely to have a major effect because only Singaporean citizens or
permanent residents can purchase HDB flats. The lack of atypical features in public housing
neighbourhoods also makes it unlikely for any first-time buyers to offer excess premiums.
The readily available transacted prices and payable premiums further decrease the possibility
of information asymmetry. The amount of time a property spends on the market does not
13
explain its persistent high cash-over-valuations. Most public housing units are similar in
configuration and have similar amenities, and studies show that nearly identical houses tend
to sell relatively quickly (Haurin et al. 2010).
Rather than the preceding two theories, we suspect that the high transaction costs in
the housing market are attributable to the high cash-over-valuations. The roles of transaction
costs in the durable goods market are best explained by Grossman and Laroque (1990), who
argue that a small amount of transaction costs can make market agents adjust their
consumption less frequently. In an extension of the theory, buyers have to shoulder the
transaction costs of the sellers to make the deals work. The transaction costs are even higher
for desirable properties because the sellers require more time and manpower to conduct a
search for better properties. We use the government’s policies on the maximum loan-to-value
ratio and minimum occupation periods as natural experiments to test the transaction cost
theory and identify the effect of transaction costs on cash-over-valuations. When buyers can
take out larger loans to finance their purchases, their transaction costs fall because they do not
have to come up with more equity when they sell their houses. When the minimum
occupation periods are raised, the options for prospective buyers decrease, thereby raising the
transaction costs in the market.
In addition to high transaction costs and information asymmetry, the irrationality of
buyers and sellers in the market may explain why buyers are paying cash-over-valuations for
properties. Arrow (1990) suggests that the psychological models of irrational decision
making posited by Tversky and Kahneman (1979) help to explain the behaviour of agents in
speculative markets (e.g., Shlefier 2000). Given the volatility and cyclical nature of housing
prices, many researchers have tested buyer rationality in the housing market. Case and Shiller
(1999) were the first to examine how people form price expectations in the United States
housing market. Based on their surveys and information on past house price movements, they
find that people are irrationally optimistic about housing prices. In a more recent study, Neo
et al. (2008) find that the excess returns in Singapore’s private housing market are positively
correlated with past returns. They attribute this finding to the buyers’ use of past appreciation
to ‘represent’ current returns, which is consistent with Case and Shiller’s (1999) findings.
These findings are important, as they imply that irrational expectations of future price
growth will be capitalised in the transacted price during the negotiation phase. Buyers are
14
willing to pay cash-over-valuations because they fear being priced out of the market in the
future. They are also optimistic that they will earn returns that justify the premiums. In
contrast, sellers set higher asking prices and cash-over-valuations to reflect the expectations
of the market.
The formation of such myopic expectations can be further associated with how
individuals ‘anchor’ on certain reference values. According to Tversky and Kahneman
(1974), individuals tend to focus on anchor values during the judgement process, and their
final estimates are insufficiently adjusted away from these anchors. Northcraft and Neale
(1987) ask professional real estate agents to assess residential properties after giving them
detailed summaries of the characteristics of the houses. After manipulating one irrelevant
variable, they find that the original listing prices described in the written materials vary by
±12% of the originally assessed market value. Seiler et al. (2013) similarly find that a
property buyer’s offer price is affected by his or her familiarity with the neighbourhood.
Lambson et al. (2004) show evidence to support the hypothesis that out-of-state buyers pay
premiums due to the anchoring effect. Using transactions from Singapore’s private housing
market, Neo et al. (2008) find empirical evidence to support their ‘anchoring’ hypothesis.
Their results suggest that buyers anchor the value of a property to the district in which the
property is located and to the property’s past appreciation. However, the proxy they use may
be tainted by changes in transport costs and the time required to travel from the central
location to one’s workplace.
Another possibility is that buyers are paying high cash-over-valuations for the
transaction utility they receive from the deal. The transaction utility measures the perceived
value of a deal, and is defined by Thaler (1985) as the difference between the amount paid
and the reference price for the good. The transaction utility theory suggests that homebuyers
are willing to pay a high premium for properties that encompass many attributes. Although
the buyers may not use some of the attributes, the theory suggests that he or she will
nevertheless pay for them. We attempt to test this hypothesis by examining whether rich
homeowners would prefer to stay near train stations. Given that rich homeowners usually
drive to work, their proximity to public transport is less important, and buyers are unlikely to
pay a premium for a facility they do not use. The rationale for doing so must come from the
transaction utility.
15
The preceding behavioural theories suggest that market agents form their reference
values of a subject property from its surrounding environment and circumstances. Any
difference between a transacted price and a valuation reflects the adjustment made to reflect
the perceived value. The higher the valuation, the less the adjustment needs to be made. In
contrast, a high valuation may signal the value of a property, and further enhance the
bargaining position of the seller. Thus, a high-value property helps its seller achieve a high
cash-over-valuation.
This paper contributes to the literature in the following ways. First, it tests the
preceding behavioural theories using a unique dataset that decreases the empirical problems
plaguing previous papers such as that by Neo et al. (2008). Most flats have similar designs
and feature access to similar amenities. This allows us to focus on testing the behavioural
theories, as the time-on-market factor is likely to be similar across properties. The market’s
settings also help us derive a valuation easily using ordinary least squares regression. The
market’s segmentation is driven by location, and that is easily controlled. In addition, the
volume of sales is substantial enough to provide similar estimates to that of the sales
comparison method.
Second, this paper is one of the first to examine the mechanism of how prices are
decided among agents with valuation information. The literature has tended to assume that
buyers form their beliefs based on past market information, their experience and their
familiarity with the market (e.g., Seiler et al. 2013). However, it seems to have omitted how
appraiser valuations affect buyer decisions. The contents of a valuation report should temper
a buyer’s expectations. However, our results show that valuations, together with agreed-upon
cash-over-valuations, form the reference points upon which buyers and sellers negotiate.
16
4
Methodology
Our empirical strategy comprises two steps. The first step involves the calculation of the
valuations of the transacted properties. We do not have the cash-over-valuations for each
property, and must rely on our transaction samples to derive their valuations. The second step
involves using the cash-over-valuations derived in the first step and regressing them against
the variables to test our hypotheses. During each step, we conduct a series of robustness tests
to ensure the validity of the results. We explain the steps in more detail as follows.
Our sample comprises 186,654 transactions made in the public housing market from
1998 to 2012. We obtained the dataset from the Singapore Institute of Surveyors and Valuers
(SISV) and Housing Development Board of Singapore. Each resale transaction is
substantiated with structural information such as the level of the unit, number of rooms, floor
area, lease commencement data, resale approval date, street name, block number and
transacted price. We further translate the addresses on each block into zip codes to calculate
their distances from amenities using ArcGIS. Given that the government conducts renewal
programmes on older buildings, we further complement the dataset with information related
to the dates on which the renewal programmes were implemented. The descriptive statistics
of the dataset are provided in Tables 1a and 1b.
Table 1a: Variables and their representation
Variable
Structural Attributes
Storey_01~05
Storey_06~10
Storey_11~15
Storey_16~20
Storey_21~25
Storey_26~30
Storey_31~35
Storey_36~40
Floor_Area~m
Room_1
Room_2
Room_3
Room_4
Room_5
HUDC
Representation
1st floor to 5th floor
6th floor to 10th floor
11th floor to 15th floor
16th floor to 20th floor
1st floor to 5th floor
16th floor to 20th floor
1st floor to 5th floor
16th floor to 20th floor
Floor Area (sqm)
One Room HDB flat
Two Room HDB flat
Three Room HDB flat
Four room HDB flat
Five Room HDB flat
Housing and Urban Development
17
Company flat
Executive flat
Undergone Main Upgrading
Programme
Executive
MUP_1_0
Neighbourhood Attributes
Distance_CBD
MRT_m
Park_m
Primary_Sc~m
Town_Centres
TopPriSch
Others
Age
Resale_Price
Year of transaction
Month of Transaction
Valuation
Distance to CBD
Distance to MRT
Distance to Park
Distance to Primary School
Distance to Town Center
Distance to Top Primary School
Age of Unit
Resale Price
Year Property transacted
Month of Transaction
Valuation of Unit
Cash Over Valuation that buyer has
to pay
Cash Over Valuation
Table 1b: Descriptive Statistics
Variable
Storey_01~05
Storey_06~10
Storey_11~15
Storey_16~20
Storey_21~25
Storey_26~30
Storey_31~35
Storey_36~40
Floor_Area~m
Age
Resale_Price
Year
of
transaction
Month
of
Transaction
Room_1
Room_2
Room_3
Room_4
Room_5
HUDC
116303
116303
116303
116303
116303
116303
116303
116303
116303
116303
116303
Mean
0.389586
0.391615
0.180511
0.025726
0.007592
0.001488
0.00012
0.000086
91.70973
16.41067
228494.3
Std. Dev.
0.487658
0.488114
0.384614
0.158317
0.086802
0.03854
0.010971
0.009272
24.59557
8.745396
83203.5
116303
2003.551
116303
116303
116303
116303
116303
116303
116303
6.757599
0.001126
0.008065
0.382114
0.39974
0.163169
0.000155
18
Min
Max
0
0
0
0
0
0
0
0
31
0
33000
1
1
1
1
1
1
1
1
239
44
850000
2.192942
1997
2012
3.487728
0.033543
0.089444
0.485906
0.489847
0.369521
0.01244
1
0
0
0
0
0
0
12
1
1
1
1
1
1
Executive
116303
Distance_CBD
116303
MRT_m
116303
Park_m
116303
Primary_Sc~m
116303
Town_Centres
116303
TopPriSch
116303
MUP_1_0
116303
Valuation
116303
Cash
Over
Valuation
116303
Source: Author Computation
0.043885
12049.28
859.7948
2341.199
374.9471
1403.331
4040.081
0.072346
222250.7
0.204841
4391.203
520.7616
1480.647
255.4374
1002.103
2585.62
0.25906
99844.36
0
0
0
0
0
0
0
0
35042.65
1
20140.05
3516.067
7928.991
3285.696
6016.038
10828.51
1
833397.1
6243.576
36922.47
-205225
87877.38
The tables show that the number of transactions is evenly distributed across the years.
Few high floor units (30th-40th floor) were transacted in the market because few high
skyscrapers were developed. In addition, the HUDC and executive housing stocks are small
compared with the three-, four- and five-room housing stocks. This is further reflected in the
collected transactions. The supply of one- and two-room HDB flats was limited as the
government discontinued the construction of such small units. Looking at the cash-overvaluation variable, some units were transacted at S$205,255 below valuation. We suspect that
this observation is an outlier, and drop it from our subsequent regressions.
Deriving the Valuation
There are several approaches to deriving property valuations. The typical ordinary
least squares method uses log prices as the dependent variable. The log linear functional form
decreases the bias resulting from the non-normal residual (Hardin and Wolverton 1996) and
makes it easier to interpret. However, there may be a selection bias (Ong et al. 2006) because
the ordinary least squares regression does not account for the sales intensity and units that are
not transacted. Sufi and Mian (2009) similarly show that a homeowner’s propensity to spend
money on housing follows the business cycle. Hence, more sales occur when prices are high.
McMillen and Weber (2008) also find that property tax assessments are more accurate if
more transactions take place, which in turn depends on changes in home prices. To overcome
the sales intensity bias, we include the number of sales in the previous period in our cashover-valuation regression.
19
Although academics use ordinary least squares regression models to conduct mass
valuations for research, appraisers prefer to use the sales comparison method. According to
this method, the appraisers use the prices of recently completed similar sales to derive the
value of a property. They adjust the prices in terms of the differences in attributes between
the subject property and the comparables. Finally, they derive the value by calculating the
weighted average of the subject property’s values derived from each comparable.
Despite the similarities between the regression and sales comparison methods, the two
share some inherent differences. For the latter, appraisers attempt to find transactions that are
as close to the subject property as possible. The variance among comparables is smaller. The
valuation becomes less accurate if the appraisers use too many comparables. In contrast,
researchers who use the ordinary least squares regression method rely on a large sample of
transactions. There is a larger variance across samples and less consistent values may be
provided. This method requires a large sample and accounts for market segmentation.
Although the sales comparison method requires a smaller sample, the decisions involved in
selecting the weights for the comparables and adjustment factors rely on experience.
Researchers have attempted to unite the approaches. Colwell, Cannaday and Wu (1983) use a
grid adjustment method, deriving the adjustment factors via the ordinary least squares
method. Isakson (1986) uses the nearest neighbours appraisal technique (NNAT), which
calculates the value through a weighted average of the actual selling price. Vandell (1991)
presents a minimum variance approach for selecting and weighting the comparable
properties. Gau, Lai and Wang (1992, 1994) provide a similar model, but replace the variance
with the variation coefficient as the measure to be minimised. However, this model presents a
challenge when the valuations of large datasets must be calculated.
Our method is similar to Isakson’s NNAT method (1986) but is less sophisticated. We
run the valuation regression using the transactions conducted in the same district as the
subject property over the past two quarters as our sample. The regression model is as follows:
𝐸(𝑉𝑎𝑙𝑢𝑒𝑖,𝑡 ) = 𝛽0 + 𝛽1 𝑆𝑡𝑟𝑢𝑐𝑡𝑢𝑟𝑎𝑙 𝐴𝑡𝑡𝑟𝑖𝑏𝑢𝑡𝑒𝑠𝑖,𝑡 + 𝛽2 𝑁𝑒𝑖𝑔ℎ𝑏𝑜𝑟ℎ𝑜𝑜𝑑 𝐴𝑡𝑡𝑟𝑖𝑏𝑢𝑡𝑒𝑠𝑖,𝑡
(1)
+ 𝛽3 𝑄𝑢𝑎𝑟𝑡𝑒𝑟𝑖,𝑡 ,
𝐸�𝑉𝑎𝑙𝑢𝑒𝑖,𝑇 � = 𝐸(𝑉𝑎𝑙𝑢𝑒𝑖, ) ∗
20
𝑃𝑃𝐼𝑇
𝑃𝑃𝐼𝑡
,
(2)
where Quarter denotes the previous quarter in which the transaction was completed, T
denotes the current period and t represents a lagged current period of either one or two
quarters. After deriving the valuation for the subject property from the regression model, we
adjust the value using the house price index from the Singapore housing authorities/HDB to
match the time at which the property was transacted. The adjustment made is based on a
perfect ex ante prediction of the future change in price.
This method is not without its limitations. First, transactions completed in
neighbouring districts may offer better comparables than the transacted properties located
within the same district for subject properties located at the district border. To ensure that the
bias is limited, we further weigh the observations based on their distance to the central
business districts and other amenities and re-run our regressions. Second, our method
deviates from past studies in that we do not weigh the comparables according to their
similarities with the subject properties in the regression model. Rather, we drop the
transactions that are completed outside the district or segment. While our method is not as
sophisticated as past methods, the availability of large numbers of transactions for each block
and the uniformity in its design help us obtain similar estimates to those complex models.
Finally, we are not privy to information on the state of the house’s condition. We attempt to
overcome the resultant bias by removing the outliers in the sample, and use the house’s age
as a proxy of its condition.
Finding the Cash-Over-Valuation and Testing the Hypotheses
After adjusting the valuations from the hedonic regressions derived from the past two
quarters, we calculate the difference between the valuation and the actual price of the subject
property that are transacted in the current period. The equations used to derive the premium
or discount and the regression model are shown as follows. To ensure that the actual cashover-valuations are reflected, we take the median cash-over-valuations for each district and
conduct a t-test with those derived from the published websites. Our test results, which are
not presented here, find no evidence that our median cash-over-valuations are different from
those published with the official figures.
Using cash-over-valuation as our new dependent variable, we re-run the log-linear
regression with the list of variables in Table 2 to test our hypotheses.
21
Table 2: Variables for Cash Over Valuation Regression
VARIABLES
Storey_06_to_10
Storey_11_to_15
Storey_16_to_20
Storey_21_to_25
Storey_26_to_30
Storey_31_to_35
Storey_36_to_40
Representation of Variables
Storey of unit transacted is 6th
floor
Storey of unit transacted is 11th
floor
Storey of unit transacted is 16th
floor
Storey of unit transacted is 21st
floor
Storey of unit transacted is 26th
floor
Storey of unit transacted is 31st
floor
Storey of unit transacted is 35th
floor
Floor_Area_sqm
Floor Area of Unit Transacted
Age
Age of Unit transacted
AgeSq
Square of Age of Unit Transacted
Room_1
1 Room HDB flat
Room_2
Room_4
Room_5
HUDC
Executive
Distance_CBD
MRT_m
to 15th
to 20th
to 25th
to 30th
to 35th
to 40th
2 Room HDb flat
4 Room HDB flat
5 Room HDB flat
HUDC unit (Bigger than 5 room HDB flat)
Executive Unit (Bigger than 5 room HDB
flat)
Distance to CBD
Distance to nearest Mass Rapid Transit
Station
CBD_Sq
Distance to CBD Squared
Park_m
Distance to nearest Park
Primary_Sch_m
to 10th
Distance to nearest Primary School
22
Purpose
Test
Hypothesis
Test
Hypothesis
Test
Hypothesis
Test
Hypothesis
Test
Hypothesis
Test
Hypothesis
Test
Hypothesis
Test
Hypothesis
Test
Hypothesis
Test
Hypothesis
Test
Hypothesis
Test
Hypothesis
Test
Hypothesis
Test
Hypothesis
Test
Hypothesis
Test
Hypothesis
Test
Hypothesis
Test
Hypothesis
Test
Hypothesis
Test
Hypothesis
Test
Hypothesis
Anchoring
Anchoring
Anchoring
Anchoring
Anchoring
Anchoring
Anchoring
Anchoring
Anchoring
Anchoring
Anchoring
Anchoring
Anchoring
Anchoring
Anchoring
Anchoring
Anchoring
Anchoring
Anchoring
Anchoring
Anchoring
Town_Centres
TopPriSch
MUP_1_0
Valuation
Lagged Sales
Cold Market
C6TO10
C11TO15
C16TO20
C21TO25
C26TO30
C31TO35
C36TO40
CFLAREA
CAGE
CAGESQ
CCBD
CCBDSQ
Distance to nearest Town Center
Distance to the top Primary School
Dummy Variable: 1- The unit
undergone refurbishment. 0 - It has not
has
The valuation of the unit
The number of sales of the block
transacted in a year for the past period
Dummy Variable : If unit is transacted
during down market, dummy variable =1
Interactive
Variable:
Cold
Market
*Storey_06_to_10
Interactive
Variable:
Cold
Market
*Storey_11_to_15
Interactive
Variable:
Cold
Market
*Storey_16_to_20
Interactive
Variable:
Cold
Market
*Storey_21_to_25
Interactive
Variable:
Cold
Market
*Storey_26_to_30
Interactive
Variable:
Cold
Market
*Storey_31_to_35
Interactive
Variable:
Cold
Market
*Storey_36_to_40
Interactive Variable: Cold Market *Floor
Area
Interactive Variable: Cold Market *Age
Interactive Variable: Cold Market *Age
Square
Interactive Variable: Cold Market *Distance
to CBD
Interactive Variable: Cold Market *Distance
to CBD squared
CR1
Interactive Variable: Cold Market *Room_1
CR2
Interactive Variable: Cold Market *Room_2
CR4
Interactive Variable: Cold Market *Room_4
CR5
Interactive Variable: Cold Market *Room_5
CRH
Interactive Variable: Cold Market *HUDC
23
Test
Anchoring
Hypothesis
Test
Anchoring
Hypothesis
Test
Anchoring
Hypothesis
Test Anchoring and
Signaling hypotheses
Control for Blocks with
few or no sales
Test for expectations
hypothesis
Test for expectations
hypothesis
Test for expectations
hypothesis
Test for expectations
hypothesis
Test for expectations
hypothesis
Test for expectations
hypothesis
Test for expectations
hypothesis
Test for expectations
hypothesis
Test for expectations
hypothesis
Test for expectations
hypothesis
Test for expectations
hypothesis
Test for expectations
hypothesis
Test for expectations
hypothesis
Test for expectations
hypothesis
Test for expectations
hypothesis
Test for expectations
hypothesis
Test for expectations
hypothesis
Test for expectations
hypothesis
CRE
CMRTD
CPARK
CPRI
CTCENTER
CTPRI
CMUP
CVAL
MRTR1
MRTR2
MRTR4
MRTR5
MRTRH
MRTRE
Y2011
Interactive
Variable:
Cold
Market
Test for expectations
*Executive Unit
hypothesis
Interactive Variable: Cold Market *Distance
Test for expectations
to MRT
hypothesis
Test for expectations
Interactive Variable: Cold Market *Car Park hypothesis
Interactive Variable: Cold Market *Distance
Test for expectations
to Primary School
hypothesis
Interactive Variable: Cold Market *Distance
Test for expectations
to town center
hypothesis
Interactive Variable: Cold Market *Distance
Test for expectations
to Top Primary School
hypothesis
Test for expectations
Interactive Variable: Cold Market *MUP
hypothesis
Interactive
Variable:
Cold
Market
Test for expectations
*Valuation
hypothesis
Test for Transaction
Interactive Variable: Distance to MRT
Utility hypothesis
station *Room_1
Test for Transaction
Interactive Variable: Distance to MRT
Utility hypothesis
station *Room_2
Test for Transaction
Interactive Variable: Distance to MRT
Utility hypothesis
station *Room_4
Test for Transaction
Interactive Variable: Distance to MRT
Utility hypothesis
station *Room_5
Test for Transaction
Interactive Variable: Distance to MRT
Utility hypothesis
station *HUDC
Test for Transaction
Interactive Variable: Distance to MRT
Utility hypothesis
station *Executive Unit
Dummy Variable that takes the value 1 if it
Test for illiquidity
is year 2011
hypothesis
The behavioural theories on anchoring and the adjustment process suggest that too
much weight tends to be placed on information considered early in the judgement process.
We expect homebuyers and sellers who apply anchoring and the adjustment process when
forming the price of a subject property to make the final price higher when the valuation is
low and vice versa. Hence, the variable value, which captures the valuation of the property
derived earlier, helps us test whether the anchoring hypothesis is supported. On the contrary,
24
if the valuation is positively correlated to the agreed-upon cash-over-valuation, it is implied
that the valuation is used to strengthen the bargaining positions of the buyers and sellers. We
further examine whether buyers and sellers anchor excess premiums based on the attributes
of the subject properties. If the attributes significantly affect the cash-over-valuation, it is
implied that the valuation report fails to help the buyer and seller adjust their reference
values. If the coefficients for those attributes-related variables are significant, the anchoring
theory is supported.
However, cash-over-valuations may reflect the transaction utility buyers have in
relation to properties. In this case, the buyers are willing to overpay because they believe they
are getting a good deal, and do so despite not using the facilities. We use interactive variables
to test this hypothesis. We multiply the variables denoting different types of housing with the
variables denoting the property’s distance away from a mass rapid transit (MRT) station.
Households in bigger units tend to own cars and are less likely to need public transport to
travel to work. However, if the proximity of such a unit to a public transport station affects its
cash-over-valuation, it is implied that the premium reflects the transaction utility.
It is likely that buyers and sellers form myopic expectations of cash premiums that are
similar to their expectations of housing returns as described by Case and Shiller (1999).
However, we consider that buyers and sellers make use of a prior agreed-upon premium as a
reference value and make adjustments from there. We test this consideration by examining
the coefficients of the variables used to represent the past quarter’s cash-over-valuations in
the regression model.
In addition to using the ordinary least squares regression method, we use the weighted
least squares regression method in our test to overcome the possible sales intensity bias
discussed previously. We thereby increase the weightage of better-selling units in the districts
compared with the lesser-selling units. We group the observations based on the number of
sales transacted in the district over the past two quarters. These transactions are used to derive
the valuations of the properties in our earlier regression. The cash-over-valuation error is
given as
𝑉𝑎𝑟(𝜀𝑖 ) = 𝜎 2 . ℎ𝑖 ,
(3)
where ℎ𝑖 represents the groups formed by sales in the district over the past two quarters.
Hence, our weighted least squares regression is
25
𝑦𝑖
�ℎ𝑖
=𝛼 .
1
�ℎ𝑖
+𝛽 .
𝑥𝑖
�ℎ𝑖
+
𝜀𝑖
�ℎ𝑖
.
(4)
By giving greater weight to groups with high sales, the regression helps us remove the
valuation error caused by lesser-selling units. If the coefficients are ultimately insignificant, it
is implied that the cash-over-valuation is caused by a lack of the comparables necessary to
derive an accurate assessment of the property value.
26
5
Results
The coefficients from our valuation regression model suggest that both neighbourhood and
structural attributes have significant effects on housing value. Consistent with the literature,
flats that are proximate to amenities, are located at a high floor level and have large floor
areas tend to fetch a premium in the market. Properties that are close to the centres of
business districts are similarly more expensive, which is consistent with the urban economics
literature. (See Table 4 for sample valuation regression results.) Because the valuations we
derived capture all of the attributes and adjustments made to reflect the time differences, we
expect any cash-over-valuation or below-valuation discounts to be random.
Table 4: A regression for valuation for one of the district for observations
VARIABLES
Log Price
Storey_01_to_05
-0.0425***
(0.00132)
0.0224***
(0.00168)
0.0701***
(0.00407)
0.0948***
(0.00764)
1.129***
(0.00780)
-0.136***
(0.00137)
-0.141***
(0.00972)
-0.0172***
(0.00307)
0.0808***
(0.00237)
0.160***
(0.00403)
-0.177***
(0.00342)
-0.0702***
(0.00131)
-0.00369**
(0.00170)
-0.00661***
Storey_11_to_15
Storey_16_to_20
Storey_21_to_25
LnFloor
LnAgeSq_10
Room_2
Room_3
Room_5
Executive
LnCBD
LnMRT
LnPark
LnPriSch
27
(0.000979)
-0.0213***
(0.00129)
-0.0259***
(0.00165)
0.00836**
(0.00331)
10.18***
(0.0505)
LnTownCentre
LnTopPriSch
MUP_1_0
Constant
Observations
R-squared
16,815
0.962
Note: The Quarter and Fixed Effects are not reported.
However, the results from the five regression models in Table 5 suggest otherwise. In
Model (1), we use the ordinary least squares method to derive the coefficients while
accounting for the period and district fixed effects. Model (2) applies the weighted least
squares technology discussed earlier to derive the coefficients. We then test each of the
hypotheses we made in the literature review using Models (3), (4) and (5). Model (3) helps us
examine how a booming market and a weakening market affect the coefficients, and Model
(4) helps us test whether the transaction utility theory is at work. Model (5) tests whether the
theory of durable good transaction costs helps to explain housing cash-over-valuations.
Table 5: Summary of results from the 5 Cash-over-valuation regression models
VARIABLES
Storey_06_to_10
Storey_11_to_15
Storey_16_to_20
Storey_21_to_25
Storey_26_to_30
(1)
lnCOV using
Ordinary Least
Squares with
Period Dummies
and District
Dummies
0.0247***
(0.00675)
0.0451***
(0.00884)
0.128***
(0.0225)
0.182***
(0.0502)
0.451***
(0.154)
(2)
(3)
(4)
Weighted Least
Testing
Test for
Squares (WLS)
Expectation
Transaction
regression using hypotheses using
Utility (WLS)
sales with
down market and with District and
District and
upmarket (WLS) Period dummies
Period Dummies
0.0250***
(0.00672)
0.0447***
(0.00882)
0.127***
(0.0217)
0.176***
(0.0423)
0.469***
(0.123)
0.137
(0.0926)
0.254**
(0.111)
0.561***
(0.166)
0.419*
(0.242)
0.675***
(0.250)
28
0.0114*
(0.00674)
0.0199**
(0.00883)
0.0525**
(0.0216)
0.0601
(0.0424)
0.116
(0.123)
(5)
Test for
Illiquidity
hypothesis
(WLS)
0.0426**
(0.0189)
0.0388
(0.0245)
0.144***
(0.0520)
0.0865
(0.0890)
0.214*
(0.128)
Storey_31_to_35
Storey_36_to_40
Floor_Area_sqm
Age
AgeSq
1.602***
(0.0925)
-0.794
(0.520)
0.0157***
(0.000443)
-0.0212***
(0.00216)
0.00121***
(5.54e-05)
1.581***
(0.223)
-0.886***
(0.325)
0.0156***
(0.000400)
-0.0209***
(0.00207)
0.00121***
(5.32e-05)
-1.07e-05
(1.12e-05)
-0.000107***
(1.11e-05)
2.51e-09***
(5.54e-10)
-3.67e-05***
(6.29e-06)
-1.57e-06
(1.50e-05)
-1.40e-05
(8.59e-06)
-1.94e-05***
(6.57e-06)
0.0669***
(0.0140)
-5.01e-06***
(1.76e-07)
-2.99e-05**
(1.48e-05)
-1.17e-05
(1.19e-05)
-0.000107***
(1.13e-05)
2.56e-09***
(5.80e-10)
-3.62e-05***
(6.41e-06)
-1.26e-06
(1.45e-05)
-1.29e-05
(8.58e-06)
-2.07e-05***
(6.53e-06)
0.0671***
(0.0137)
-4.93e-06***
(1.44e-07)
-2.57e-05
(1.73e-05)
Room_1
Room_2
Room_4
Room_5
HUDC
Executive
Distance_CBD
MRT_m
CBD_Sq
Park_m
Primary_Sch_m
Town_Centres
TopPriSch
MUP_1_0
VAL
LSALES
COLDMARKET
C6TO10
C11TO15
C16TO20
29
1.261***
(0.338)
-1.012**
(0.425)
0.0131**
(0.00599)
-0.0590**
(0.0275)
0.000768
(0.000585)
-0.0774
(0.455)
-0.184
(0.289)
0.321
(0.205)
0.785**
(0.321)
0.711**
(0.314)
1.016**
(0.457)
-6.44e-05
(7.29e-05)
-0.000350***
(0.000105)
-2.19e-09
(3.97e-09)
-0.000125***
(4.07e-05)
0.000123
(0.000151)
-8.52e-05
(5.33e-05)
1.59e-05
(4.66e-05)
-0.0586
(0.161)
-6.92e-06***
(1.30e-06)
-0.000340***
(1.66e-05)
-2.501***
(0.814)
-0.0933
(0.0929)
-0.173
(0.111)
-0.308*
(0.168)
0.897***
(0.222)
-1.571***
(0.326)
0.515**
(0.205)
-2.122***
(0.266)
-0.00853***
(0.00206)
0.000951***
(5.35e-05)
0
(0)
-0.158***
(0.0556)
0.293***
(0.0163)
0.495***
(0.0271)
-1.303
(1.804)
0.715***
(0.0398)
-3.96e-06
(1.20e-05)
-3.88e-05***
(1.36e-05)
2.56e-09***
(5.84e-10)
-2.91e-05***
(6.45e-06)
4.92e-06
(1.46e-05)
8.46e-06
(8.64e-06)
-2.33e-05***
(6.57e-06)
0.113***
(0.0138)
-2.62e-06***
(1.25e-07)
-2.40e-05
(1.74e-05)
0.0353***
(0.00584)
0.000206
(0.000145)
-0.390*
(0.199)
-0.204**
(0.0893)
0.0792**
(0.0329)
0.207***
(0.0510)
0
(0)
0.176*
(0.0907)
1.90e-05
(2.98e-05)
-2.43e-06
(3.10e-05)
1.32e-09
(1.58e-09)
5.00e-05***
(1.75e-05)
-0.000127***
(4.29e-05)
-2.00e-05
(2.31e-05)
3.52e-05*
(1.87e-05)
-0.0549
(0.0487)
-5.96e-07
(3.76e-07)
-0.00494***
(0.00122)
C21TO25
-0.0151
(0.246)
-0.122
(0.307)
0
(0)
0
(0)
0.0178***
(0.00601)
-0.0282
(0.0276)
0.00128**
(0.000587)
-1.33e-05
(7.21e-05)
6.66e-09*
(3.94e-09)
-0.378
(0.462)
-0.117
(0.291)
-0.410**
(0.206)
-0.682**
(0.322)
0
(0)
-0.451
(0.458)
0.000163
(0.000105)
5.61e-05
(4.03e-05)
-9.25e-05
(0.000151)
3.11e-05
(5.27e-05)
-2.76e-05
(4.64e-05)
0.0618
(0.162)
-5.32e-06***
(1.31e-06)
C26TO30
C31TO35
C36TO40
CFLAREA
CAGE
CAGESQ
CCBD
CCBDSQ
CR1
CR2
CR4
CR5
CRH
CRE
CMRTD
CPARK
CPRI
CTCENTER
CTPRI
CMUP
CVAL
MRTR4
-7.93e-05***
(1.39e-05)
-4.08e-05**
(1.89e-05)
0.0141
(0.00979)
MRTR5
MRTRH
30
MRTRE
8.80e-09
(1.49e-08)
Y2011
Constant
10.56***
(0.109)
10.53***
(0.0981)
13.57***
(0.814)
10.48***
(0.106)
0.661***
(0.0761)
9.111***
(0.243)
70,827
0.304
70,827
0.305
70,827
0.208
70,827
0.299
4,187
0.398
Observations
R-squared
The coefficients in Models (1) and (2) indicate that buyers tend to pay more for units
that are located at higher levels and have bigger floor areas. For instance, a unit on the 6th10th storeys of a building commands an additional 2.47% cash-over-valuation than a unit
located on the 1st-5th storeys. One additional square metre of floor area similarly commands
an additional 1.5% cash-over-valuation. Further, homeowners tend to overpay for units that
are located near parks, MRT stations and top primary schools. If a property is located 100 m
closer to an MRT station, the buyer must fork out an additional 1% cash-over-valuation to
close the transaction. The relationship between age and the excess premium one pays for a
flat is quadratic and follows a U shape. This U-shape relationship reflects the trade-off
between location and obsolescence. Old apartments are usually located closer to town
centres, and new apartments are usually located farther away. The coefficients for a unit’s
proximity to a town centre and distance away from the central business district are
interestingly not found to be significant. This result differs from Neo’s (2008) study of the
private market. Nevertheless, our findings support the anchoring theory and the hypotheses
we set out earlier.
In the same regression models, the valuation coefficient is negative and significant.
Given a high valuation, sellers are more willing to accept low cash-over-valuations and vice
versa. This provides further proof of the anchoring theory, and indicates that buyers and
sellers do not adjust their reference values despite knowing the valuations. In addition, the
results dispel the hypothesis that sellers use high valuations to strengthen their bargaining
positions.
The results from Model (3) indicate that buyers and sellers form myopic expectations
based on past agreed-upon cash-over-valuations. In Model (3), we interact the variables with
a dummy variable that takes the value of 1 when the market is in a downturn. The cash-overvaluation is 2.5% less in a weakened market. In addition, the excess premium for bigger
31
rooms is less when the market is weak. However, the excess premium associated with
amenity-related attributes in a cold market is not significantly different from that in a hot
market. We further test the stickiness of cash-over-valuations, as the myopic expectations
theory hypothesises that individuals form expectations based on past returns. The results,
which are presented in Table 6, indicate that sellers and buyers are myopic in negotiating
cash-over-valuations.
Table 6: Test for Stickiness for Cash over valuation
COV
Lag COV
Constant
Coef.
0.0485
31
26661.
88
Robu
st Std.
Err.
7.02E
-05
2.796
38
t
|
691.6
1
9534.
43
P>|t
[95%
Interva
Conf.
l]
0.0483
0.0486
0
9
72
26656.
26667.
0
26
5
In addition to the behavioural theories at work, we suspect that buyers pay an excess
amount of money for the transaction utility involved. One of the implications of transaction
utility theory is that homeowners pay premiums for facilities they may not need. We test the
hypothesis set by the transaction utility theory in Model (4) by interacting the flat-type
variable with the distance-to-MRT variable. Our rationale is that owners who stay in big flats
(i.e., four- and five-room HDB flats) are richer and more likely to own cars compared with
those who stay in three-room HDB flats. As they have cars, these owners are unlikely to use
MRT and less likely to pay a premium for a location near an MRT station. However, if richer
owners are nevertheless paying for MRT station proximity, it is implied that buyers pay for
the enhancement of the whole package provided by a subject property. This supports the
transaction utility theory. Using three-room HDB flats as our base, we find that the
coefficients are significant and negative for owners staying in four- and five-room HDB flats.
The coefficients for owners staying in executive units and HUDC flats are also insignificant.
These results strongly support the theory that buyers pay for the transaction utility involved.
The premium paid by owners staying in big flats in proximity to MRT is at least as high as
that paid by owners staying in smaller units.
32
Finally, we examine whether illiquidity and lower down payments also lead to the
payment of higher cash-over-valuations, as shown in Model (6). Our sample comprises
transactions made between 2009 and 2011, and we introduce the Y2011 dummy variable into
the model. The Y2011 dummy takes a value of 1 if the observed transaction was completed
in 2011, and is used as a proxy for the increase in transaction costs. During 2010, the
government announced an extension of the minimum occupation period for new owners who
purchased flats in the resale market from 2 and a half years to 5 years. This announcement
will greatly affect the flexibility of buyers in terms of their future family planning and
decrease the pool of public flats on sale. In other words, buyers must incur higher transaction
costs to close their deals. Because the housing market enjoyed upturns during 2009 and 2011,
market forces make any difference in effects unlikely. Using Model (6) in Table 5, we verify
the link between transaction costs and cash-over-valuations. The increase in transaction costs
in 2010 is strongly associated with the high cash-over-valuations in 2011. Although the
results corroborate the illiquidity and transaction costs theory, the test is not sufficiently
clean. We require additional micro data on the buyers and sellers to conduct a better test.
33
6
Conclusion
This study attempts to identify the reasons for buyers’ willingness to pay premiums above
appraisers’ valuations. Our empirical results strongly suggest that cash-over-valuations are
strongly driven by the irrational behaviour of buyers and sellers. First, cash-over-valuations
are anchored on the structural and neighbourhood-related features of subject properties, and
valuation reports do not temper reference values. Second, buyers and sellers form myopic
expectations of current cash-over-valuations using past agreed-upon excess premiums. The
excess premium one is willing to pay depends on the state of the market. Third, buyers tend
to accept high cash-over-valuations for properties packed with favourable attributes even
when they do not require all of them.
The results of our study have significant policy implications for the government. As
they are sticky over time, cash-over-valuations are probably not good indicators of the
effectiveness of government intervention, especially if the policies are intended to fine-tune
the housing market. In addition, the policies’ effects are unlikely to be observed in the short
term, as buyers are entrenched in their beliefs. Changing perceptions in the short term usually
requires the implementation of harsh policies, which may do the market more harm than
good.
To examine how government policies influence the decision heuristics of buyers and
sellers, we require a micro dataset that contains demographic information related to buyers,
sellers and real estate agents. Future research on the mobility options of owners will further
aid our understanding of the behavioural constructs of buyers and sellers. In addition, future
directions on housing heuristics may rely on the field experiments conducted in developing
countries to observe the behavioural variations among buyers with different information sets.
34
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36