The impact of construction quality on house prices

Journal of Housing Economics 26 (2014) 126–138

Contents lists available at ScienceDirect

Journal of Housing Economics journal homepage: www.elsevier.com/locate/jhec

The impact of construction quality on house prices q Joseph T.L. Ooi ⇑, Thao T.T. Le, Nai-Jia Lee National University of Singapore, Singapore

a r t i c l e

i n f o

Article history: Received 10 September 2013 Revised 24 September 2014 Accepted 17 October 2014 Available online 28 October 2014 JEL classification: R21 R31 Keywords: Construction quality Housing Price inflation

a b s t r a c t This paper examines the impact of workmanship and construction quality of new housing on their sale price and capital growth. To measure construction quality, we take advantage of a unique situation in Singapore where newly completed residential projects are assessed independently on their workmanship under the Construction Quality Assessment System (CONQUAS). Examining 100,593 sale transactions of apartment units in 205 residential developments completed between 1998 and 2010, we find strong evidence that their selling price and appreciation rate are related significantly to construction quality of the new homes. Moreover, the ‘‘quality’’ premium is present in both the primary and secondary markets. The empirical evidence suggests that apartments that are well constructed not only command a higher price for developers, but they also generate higher capital gains for homeowners and investors in the future. Ó 2014 Elsevier Inc. All rights reserved.

Those who design and construct high-quality buildings may follow three strategies. One strategy is passive, continuing to provide good quality and hoping that there will be a future although uncertain rewards from a good reputation. The second strategy is to aim directly at the premium and provide easily digested information in a standardized form that would influence the price paid for the facility. The third strategy is to acknowledge that real estate funds and similar investors are more occupied with the analysis of taxation and incentives for fund managers than with the technical quality of built facilities. . .’’ [Brochner (2011)]

q We thank G. Ofori, K.O. Lee, M. Mori, T.F. Sing and participants at the NUS IRES-DRE seminar for their valuable inputs and comments. We also like to thank Piet Eicholtz and the participants at the seminar held in Maastricht University for their comments and insights. We also thank the editors and reviewers for their helpful inputs and comments. The main author received financial assistance from National University of Singapore for this research project. ⇑ Corresponding author. E-mail addresses: [email protected] (J.T.L. Ooi), thanhle.thao@gmail. com (T.T.T. Le), [email protected] (N.-J. Lee).

http://dx.doi.org/10.1016/j.jhe.2014.10.001 1051-1377/Ó 2014 Elsevier Inc. All rights reserved.

1. Introduction The quality of a house can be defined is different ways. Many researchers have modeled housing quality as a function of the neighborhood characteristics and structural attributes of the houses. At the neighborhood level, housing quality is often associated with local amenities and living conditions. At the unit level, housing quality is frequently restricted to structural attributes that could be measured objectively, such as the house’s built-up area, number of bedrooms, number of bathrooms, and land area. Numerous studies have also identified the premiums attached to the individual attributes using the hedonic regression methodology (Rosen, 1974). Besides these neighborhood and structural attributes, buyers of new homes are particularly concerned with the quality of construction and workmanship, i.e. the number of defects in the completed units. Hence, it is not surprising that several authors have posited that buyers will pay a premium for high quality homes. For example, Wieand (1983) postulates that the occurrences of defects or breakdowns reveal poor quality components, and in themselves indicate undesirable conditions which the occupants will attempt to

J.T.L. Ooi et al. / Journal of Housing Economics 26 (2014) 126–138

avoid or will only accept the dwellings at reduced rent.1 Baum (1993) also hypothesizes that ‘‘front-loading’’ the construction cost improves building quality and reduces the maintenance costs for the occupier, which may in turn lead to higher rental value and/or and lower capitalization rate for the building. Similarly, Ong (1997) argues that if the public and buyers can observe the product quality, they will pay a market price which commensurate with the quality. There are many anecdotal incidents of homebuilders advertising the construction quality of their developments in the hope of gaining an edge over competing projects. However, empirical evidence on the impact of construction quality on housing price is limited due to the difficulty in quantifying the quality of workmanship and construction.2 This paper attempts to fill the gap in the literature by taking advantage of a unique situation in Singapore where the workmanship quality of new buildings is assessed independently by regulators throughout their construction phase until physical completion. Under the Construction Quality Assessment System (CONQUAS) scheme, the assessment covers three main components, namely structural works, architectural works, and mechanical and electrical (M&E) works. An aggregate CONQUAS score (out of 100%) is given for each completed building. Focusing on the private residential market, we collected the construction quality scores of 205 new housing projects in Singapore. The projects were completed within a time span of 13 years from 1998 to 2010. Combining this information with another database containing the sale transactions of new and resale units over the study period, this unique data set allows us to evaluate the impact of good construction quality on prices in the primary and secondary housing markets. Two primary research questions guide our empirical tests. First, do homebuyers pay a premium for new homes with good construction quality, and if so, by how much? Second, is it worthwhile for homebuyers to pay more for quality homes? These questions are not trivial. First, new residential projects are capital intensive and measures required to improve construction quality (either through prevention measures such as proper training of personnel, and process analysis prior to the start of each job; or through appraisal processes such as routine monitoring and benchmarking using systematic checklists at various stages of the project) incur additional costs.3 If good construction quality is not 1 The brokerage literature further suggests that the condition of homes can be inferred from their time-on-market with a longer time-on-market associated with lower housing quality. The intuition for this argument is that if a house has stayed on the market for a long time, potential purchasers are likely to suspect that the property has some flaws which are not apparent to them but have been detected by earlier visitors (Taylor, 1999; Chen and Rutherford, 2012). 2 In the field of construction management, the three standard performance measures for project assessment are cost, schedule and quality. Unlike the first two measures which are quantitative in nature, quality is subjective (see Low and Yeo, 1998; Yasamis et al., 2002). 3 From the contractor’s perspective, effective project control involves a multitude of tasks including the meticulous management of procurement, cost, schedule, quality, workforce and safety. Kim and Kim (2011) highlighted that inspection is one of the critical processes in quality control for ensuring that final products fulfill the defined requirements assigned by the customer. However, quality inspection requires enormous amounts of time and effort from inspectors and contractors. Rosenfeld (2009) classifies them as costs of quality, i.e. the cost of measures taken to ensure and achieve a satisfactory level of quality.

127

capitalized into housing prices, there is no incentive for the profit-maximizing homebuilders to produce good quality homes. This may consequently lead to a ‘‘lemons’’ market where only low quality of housing are provided (Akerlof, 1970). From a policy perspective, this is certainly not desirable, which may then necessitates the regulators intervening through incentives and or penalties to ensure that construction quality in the housing development sector is not compromised. From the property appraisal literature, the premium for a marginal improvement in the construction and workmanship quality of housing is rarely incorporated in the appraiser report too. Moreover, house price changes affect many households’ wealth holdings because home equity constitutes a large percentage of household wealth (Le Blanc and Lagarenne, 2004). House price appreciation can be tapped and makes trading up in the housing market easier, likely increasing turnover sates in the residential market (see Bourassa et al., 2009). Variations in residential property prices also affect the risk level of a homeowner’s portfolio (Englund et al., 2002). Hence, it would be insightful to calibrate to what extent capital appreciation of housing is related to construction quality. This line of enquiry extends a stream of the literature which finds that price appreciation of houses is not homogenous (see Smith and Tesarek, 1991; Archer et al., 1996; Smith and Ho, 1996; Clapp and Giaccotto, 1998; Coulson and Lahr, 2005; Harding et al., 2007; Bourassa et al., 2009; Ferreira and Gyourko, 2012). We first identify the marginal value of construction quality by analyzing a total of 100,593 sale transactions of multi-family dwelling units with varying CONQUAS scores. Standard hedonic equations are employed to control for building and unit heterogeneity as well as real estate market conditions and other location-, time- and developer-fixed effects. Focusing on the value of good construction, we introduce additional variables into the hedonic pricing function to capture the effect of construction quality. Our results show a positive and significant positive relationship between house prices and CONQUAS scores, indicating that, all else being equal, a premium exists for construction quality. Specifically, a one standard deviation improvement in the CONQUAS score increases the selling price of an average home by 2.92%. It is interesting to observe that the premium on construction quality exists in the presale market, whereby houses are purchased off the drawings, i.e. before they are physically built. This is so despite that the CONQUAS scores are only ascertainable after the new homes are physically built. Our analysis suggests that the score of previous projects built by the same homebuilders is a good predictor of the construction quality for their next development project. We also observe that the construction quality premium persisted in the resale (secondary) housing market. This implies that homebuyers who have paid a high price for good quality homes can at the least recoup the premium on construction quality when they resell the houses in the secondary market. In fact, the effect of quality in the resale market is almost twice that in the presale market. New homes in the presale market can enjoy a 0.36% premium in sale prices for every additional CONQUAS score, while existing homes will sell for 0.69% more. An average

128

J.T.L. Ooi et al. / Journal of Housing Economics 26 (2014) 126–138

house in our sample, which is sold at about $1,000,000, can raise its price by $21,456 and $41,124 in the presale and resale market, respectively, if its CONQUAS score is one standard deviation higher than the average score. The lower premium attached to construction quality of uncompleted homes could be attributed to either a discount in the presale market due to uncertainty in the eventual construction quality scores, or the value of good construction quality homes appreciating faster over time. To probe deeper, we next examine how construction quality affects the price appreciation of new housing over time. Prior studies have shown that houses of different quality and location do not appreciate at the same rate. The null hypothesis is that the returns of high construction quality homes are not statistically different from the average market return. To test this hypothesis, we employ a generalized version of the repeat-sales model to estimate the house price appreciation for 23,843 pairs of housing sales in our sample. Controlling for the overall price movement in the market, we observe that prices of high quality homes appreciated significantly more than the average quality homes. This confirms that homebuyers who have paid for good quality homes earned a higher return than buyers who have purchased lesser quality homes over the same period. This paper is organized as follows: Section 2 reviews prior studies in the literature which have examined the relationship between quality and housing prices and or rentals. Section 3 outlines the objective measure of construction quality we used in the research, and describes the sample used for the study. Sections 4 and 5 present separately the research methodology and empirical results for each of our two research questions. Section 6 concludes with a summary of the main findings and their implications.

2. Literature review It is widely recognized that home buyers are jointly purchasing a wide variety of services at a particular location, such as a certain number of square feet of living space, different kinds of rooms, a particular structure type, an address, accessibility to employment, a neighborhood environment, a set of neighbors, and a diverse collection of public and quasi-public services including schools, garbage collection, and police protection. However, as noted by Kain and Quigley (1970), the difficulty in measuring the ‘‘quality’’ of a dwelling unit, that is how they impact the occupiers’ utility, is perhaps the most vexing problem encountered in evaluating the attributes of bundles of residential services. Researchers try to determine the contribution of specific housing attributes to property values or rentals by moving to a regression framework. However, for these attributes to be included in the hedonic pricing models, they need to be measurable. Kain and Quigley (1970) were amongst the firsts to recognize ‘‘quality’’ as an individual attribute contributing to the value of a property. They employ data from three separate surveys of around 1500 households and dwelling units in the city of St. Louis completed in the summer of 1967. In the first survey, interviewers were instructed to

rate the quality of a particular aspect of each dwelling unit, for example, the walls, on a scale ranging between 1 (‘‘excellent condition’’) and 5 (‘‘requires replacement’’). A second survey conducted by city building inspectors provided quality ratings for specific aspects of the exterior of each sample structure. In the third survey, building inspectors also rated the quality of the block facing both sides of the streets on which the sample dwelling units were located. The combined surveys provided 39 separate variables indicating the quality of the sampled dwelling units, which are then consolidated through factor analysis into five composite factors, representing qualities of the ‘‘basic residential’’, ‘‘dwelling unit’’, ‘‘nearby properties’’, ‘‘nonresidential use’’, and ‘‘structure’’, respectively.4 In their hedonic regressions, three out of the five quality measures were statistically and economically significant in affecting housing rental values. Moreover, they find that the physical and environmental quality of dwelling units has about as much effect on the price of housing as the number of rooms, number of bathrooms, and lot size.5 In another study covering twenty U.S. cities between 1974 and 1976, Wieand (1983) observes that housing quality is an important factor in rental consideration. He measures housing quality by the probability-of-defect. This is derived from the Annual Housing Survey which asked the survey respondents about the presence of specific defects such as incomplete plumbing, broken kitchen appliances, or basement leaks in their apartments. Ommeren and Koopman (2011) recent study on public housing in Rotterdam also find that maintenance quality is negatively related to residential moving probabilities whilst positively connected with rent. Categorizing the sampled apartments into high-, average- and low-quality (based on maintenance condition obtained from the owners), the authors find that households are willing to pay a rent increase of 6.8% to move from a low-quality apartment to an average-quality one, and 6.7% more to move from an average-quality unit to a high-quality one. The above studies have generally focused on the condition of existing homes, which is largely related to their state of maintenance and disrepair.6 Building age has been employed as an instrument of the otherwise unobservable housing quality. This is on the basis that building age and 4 ‘‘Basic residential quality’’ variable measures the overall quality of the exterior physical environment such as overall condition of the structure and parcel, landscaping, cleanliness of the parcel and block face, conditions of streets, walks and driveways. ‘‘Dwelling unit quality’’ variable represents the structural condition and housekeeping inside the sample dwelling unit. ‘‘Quality of proximate properties’’ variable accounts for the cleanliness, landscaping, and condition of nearby properties. ‘‘Non-residential use’’ variable measures the presence and effect of commercial and industrial land uses in the immediate vicinity. ‘‘Average structure quality’’ variable measures the average quality of structures on the block face. 5 The coefficients suggest an increase of $7.22, $4.02 and $2.08, respectively, for each additional quality unit – a substantial effect given that the mean value of contract rents of their sample is $63.19. Similarly, testing with the property owners sample confirms that they are also willing to pay $750–$1400 more for properties with higher quality. 6 Using data on 1.8 million house transactions in Massachusetts, Campbell et al. (2011) recently find that houses sold close to the death of at least one of the seller are sold at 5–7% lower prices than other houses. They attribute the death-related discounts to result primarily from poor maintenance of single-family housed by older sellers.

J.T.L. Ooi et al. / Journal of Housing Economics 26 (2014) 126–138

quality are highly correlated (that is, newly built houses tend to be in better physical shape than older houses). Nevertheless, Baum (1993) and Harding et al. (2007) have cautioned that it would be incorrect to relate the age of structure to quality, especially for a property that has been substantially refurbished or diligently maintained by its owners.7 Moreover, self-assessment by owners may understandably overstate the quality of their properties. Occupiers’ survey are also subjected to disparities in the respondents’ view on what constitute good quality or their diligence in reporting defects.8 In sum, prior studies that have attempted to relate ‘‘quality’’ to prices or rentals of existing homes focused primarily on the maintenance standard of the dwellings.9 However, researchers face a potential endogeneity problem because expensive homes are likely to have better quality. For example, the literature argues that rent controls have an adverse effect on the quality of housing because landlords are discouraged from investing in the maintenance of their properties (Olsen, 1988; Gyourko and Linneman, 1990; Moon and Stotsky, 1993; Sims, 2007). In addition, empirical research in this area is plagued by the lack of an objective measure of what is good (or bad) construction quality. Adopting an objective measure of construction and workmanship quality of new private housing developments in Singapore, our study is the first to examine explicitly the influence of construction quality on housing prices and returns. 3. Construction quality We measure construction quality using the CONQUAS scoring metric, which has been recognized in construction economics literature as an objective measure of construction workmanship quality (Ling, 2005). The scoring metric was first introduced in 1989 by the Building Construction Authority (BCA) as a standardized method of quality assessment for homebuilders to set targets for contractors and to grade the quality of their finished buildings.10 Adopted as the de facto yardstick for measuring the workmanship quality of building projects in Singapore, all public-sector projects with a gross floor area exceeding 5000 m2 are required to be assessed under the CONQUAS. Private-sector projects built on leasehold sites acquired from the State are 7 Baum (1993) contends that by measuring building depreciation and developing a classification of building qualities, it is possible to relate qualities to depreciation in order to prove a stronger and more meaningful relationship exists between quality and depreciation than that which exists between age and depreciation. Harding et al. (2007) further argue that the rate of decay is slower for homeowners who maintain their homes. 8 As noted by Lichtenstein and Kern (1987), quality measures obtained from tenants are unreliable and subject to serious measurement error because evaluation of quality involves subjective judgment. Consequently, different tenants are unlikely to apply consistent standards. 9 The contribution of architectural design, which is commonly linked to the aesthetic appearance and internal spatial configuration of the housing project, to the value of buildings has been evaluated empirically by Vandell and Lane (1989). The results of their study confirm a strong influence of design on rents; structures rated in the top 20% for design quality were predicted to extract almost 22% higher rents than those rated in the bottom 20%. 10 Oakland and Marosszeky (2005) report that property developers have used the CONQUAS score to drive improvement and to differentiate themselves from their competitors.

129

also assessed. Other private developers may also request for their buildings to be assessed (Ofori and Gang, 2001).11 Other countries that have since adapted the CONQUAS successfully include United Kingdom and Hong Kong. The CONQUAS assessment system consists of three main components: Structural Works, Architectural Works, and Mechanical and Electrical (M&E) Works within each component are further divided into different items for assessment. Structural Works comprises: (i) Site inspection of formwork, steel reinforcement and finished concrete during construction. The assessment includes structural steel and pre-stressed concrete if each constitutes more than 20% of the total structural cost; (ii) Laboratory testing of compressive strength of concrete and tensile strength of steel reinforcement; and (iii) Non-destructive testing of the uniformity and the cover of hardened concrete. The structural integrity of the building is of paramount importance as the costs of failure and repairs are very significant. Architectural Works deals mainly with the finishes and components. This is the part where the quality and standard of workmanship are most visible. The assessment covers: (i) Site inspection of internal finishes, roofs, external walls and external works at the completion stage of the building. Internal finishes include floors, internal walls, ceiling, doors, windows and components (architectural works that are not classified above); and (ii) Material & Functional tests such as water-tightness of windows and external walls and adhesion of internal wall tiles. M&E Works covers Air-conditioning & Mechanical Ventilation Works (ACMV), Electrical Works, Fire Protection Works, the Sanitary & Plumbing Works and the basic M&E fittings. Their inclusion in the scoring is in view of their increasingly high cost proportion and impact on the performance of a building. The stages of the assessment include: (i) Site inspection of in-process works during construction such as on ACMV ductworks, electrical conduits, concealed pipes; (ii) Site inspection of final installed works such as the Air-Handling Unit (AHU), the cooling tower, fire alarm control panel; and (iii) Performance tests on selected works such as Water Pressure Test, Earthing Test, Dry Riser Test. All assessments are carried out independently by assessors from the Quality and Certification Department of BCA. For Structural and M&E Works, the assessment is carried out throughout various construction stages of a project. For Architectural Works, the assessments are carried out on the completed building.12 The weightage for private housing projects are allocated as follows: Structural Works (25%), Architectural Works (65%), and M&E Works (10%).13 11 The fee for CONQUAS assessment can range from $10,600 (for developments with total floor area less than 5000 sqm) to $126,000 (for developments with total floor area more than 110,000 sqm). Among all 999-year and freehold residential projects that were completed between 1998 and 2010 in Singapore, less than 40% do not have CONQUAS scores. 12 The assessment includes tests on the materials and the functional performance of selected services and installation. These tests helps to safeguard the interest of building occupants in relation to safety, comfort and aesthetic defects, which surface only after sometime. Information on the CONQUAS is available on the BCA’s website: http://www.bca.gov.sg. 13 The weightage system, which is a compromise between the cost proportions of the three components in the various buildings and their aesthetic consideration, is aimed at making the CONQUAS score objective in representing the quality of a building.

130

J.T.L. Ooi et al. / Journal of Housing Economics 26 (2014) 126–138

Table 1 Descriptive statistics of CONQUAS scores by project characteristics. Mean

Median

Std. deviation

Minimum

Maximum

No. of projects

83.4

82.5

6.1

62.5

96.2

205

78.9 82.4 87.3

79.6 82.1 87.8

4.0 5.3 5.5

67.4 62.5 69.0

86.5 94.1 96.2

61 60 84

By land tenure Freehold Leasehold

84.7 82.0

84.8 81.3

5.7 6.3

69.0 62.5

96.2 94.8

103 102

By project size <200 units 201–500 units >500 units

83.4 83.1 83.8

83.5 82.5 82.5

6.7 5.9 5.8

62.5 69.0 69.1

94.8 96.2 95.6

73 92 40

By location Prime location Non-prime location

85.8 82.2

86.6 81.8

5.9 5.9

72.6 62.5

94.8 96.2

66 139

All projects By completion year 1998–2001 2002–2005 2006–2010

This table presents the descriptive statistics of the CONQUAS scores of 205 private residential developments completed in Singapore between 1998 and 2010. The scores shown below are in percentage terms and the highest score a development can get is 100%.

The CONQUAS score for a building is the sum of points awarded to the three components. Based on a maximum score of 100%, a score of 80 means 80% of the items checked for workmanship quality met the CONQUAS standards. Thus, a building that achieves a higher score is better constructed, in terms of workmanship quality, than a building with a lower score. The assessment standards are fine-tuned periodically and the last major revision carried out in 1998 when CONQUAS 21 was launched. For consistency, this marks the beginning of our sample period. In total, we managed to collect the CONQUAS scores of 205 new residential projects that were completed between 1998 and 2010. Table 1 reports the descriptive statistics of the CONQUAS scores for the full sample as well as subsamples, which are partitioned according to the land tenure, year of completion, and size of developments. The average project in the sample has a score of 83.4.14 We see an obvious rising trend over the study period, with the average score improving from 78.9 during 1998–2001 to 87.3 during 2006–2010. This trend is confirmed in Fig. 1, which plots the number of residential projects completed annually between 1998 and 2010 and the average CONQUAS scores of all projects completed in each year over the study period. The rising trend indicates that construction quality of new private housing projects has, on average, improved over time, possibly due to a shift in demand for good construction quality by homebuyers, or due to the developers and contractors becoming more familiar with the CONQUAS assessment criterion. In Singapore, residential properties can be built on land with either a freehold or leasehold tenure. Freehold properties are usually more expensive because the land is owned by the owners in perpetuity. The tenure of leasehold properties, on the other hand, is limited, commonly

Fig. 1. Number of developments and their average CONQUAS scores, 1998–2010. This figure plots the number of new private residential projects completed annually between 1998 and 2010 (right axis). The scores shown on the left axis are the average scores of the projects completed in each year.

to 99 years in Singapore. For the purpose of our study, residential developments with longer lease tenure of 999years are classified as freehold properties. Table 1 illustrates that the construction quality of the average freehold project is 270 basis points higher than the average leasehold project with the difference being statistically significant at the 1% level. In addition, the CONQUAS scores are significantly higher for residential projects in planning districts 1–4, and 9–11, which are traditionally regarded as the prime residential locations in Singapore (see Fig. 2). There is, however, no significant difference in the CONQUAS scores for huge and small development projects.

4. Construction quality and house prices 14

The top three developments ranked by CONQUAS scores in our sample are: The Arte (96.2), The Cascadia (95.6) and City Squares Residence (95.6). At the other end, the bottom three developments are: The Trumps (62.5), Hilltop Grove (67.4) and Starville (69.0).

In this section, we examine the impact of good construction quality on the sale prices of new homes. To explore how construction quality affects property values,

J.T.L. Ooi et al. / Journal of Housing Economics 26 (2014) 126–138

131

Fig. 2. Location map of sampled projects. This figure plots the locations of the 205 sampled private residential developments in relation to the Central Business District (CBD), which is outlined in red, and the planning districts, which are numbered from 1 to 28. The prime residential areas in Singapore are located in districts 1–4, and 9–11. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

we begin by estimating a standard hedonic price model with an additional variable measuring the construction quality score. In general, the price model is

Pi ¼ Pxi þ mi ;

ð1Þ

where Pi is the natural log of the real selling price of the ith property, xi is a vector of explanatory variables containing physical and location characteristics, development specific attributes, and the construction quality score. The error term, mi is assumed to be normally distributed with mean zero and variance r2v . Our sample for the hedonic regressions comprises 100,593 sale transactions in the 205 developments sold between 1998 and 2011. The data on the sale price, contract date, and floor area for each unit sold are obtained from Real Estate Information System (REALIS), a data set maintained by Singapore’s national land use planning authority, the Urban Redevelopment Authority (URA). The transaction price is the agreed purchase price of the property, and excludes stamp duties, legal and agency fees, and other professional fees. The prices are then adjusted using the property price index to 1998. Table 2 defines and presents the descriptive statistics of the variables used in the empirical model for the entire sample. Monetary values are expressed in Singapore dollars throughout. The average unit in our sample was sold at slightly above S$1 million and has a floor area of 122.4 m2 (1317 ft2). About 37% of the sampled units are built on freehold or 999-year leasehold land, as opposed to 63% being built on 99-year leasehold land. Signifying the popularity of condominiums in Singapore, 85% of the sale transactions involved condominium units.15 Singapore occupies a small physical area. 15 The term ‘‘condominium’’, as used in the Singapore context, is a planning term which refers to residential projects developed on sites of at least 40,000 sq ft, and the residents share the communal facilities. It is not to be confused with the legal term used in the US, which refers to strata title ownership of multi-family apartments where the land and common areas are co-owned by all the homeowners. Thus, non-condominium projects (i.e. residential projects developed on sites smaller than 40,000 sq ft in which the residents still share the communal facilities) can still be sold in Singapore.

For each of the developments in our sample, we also measure the distance to the Central Business District (CBD) and the closest subway station. The greatest distance to a subway station is only 6.6 km. Similarly, the development farthest away from the CBD is only 17.9 km. Presale transactions refer to units sold by the homebuilders to the first buyers prior to the physical completion of the development.16 Sub-sale transactions refer to the first buyers (who purchased their unit from the developer) selling the uncompleted units to third parties. Notably, 55% and 12% of the sale transactions occurred in the presale and sub-sale markets where the new housing units have not been physically completed. The balance 33% of the sample is resale transactions which involved owners selling their physically completed units in the secondary market. To control for sales in the presale and sub-sale markets, we incorporate two additional variables in the regression models, namely NEWSALE and SUBSALE, representing sale of uncompleted new housing by homebuilders, and resale by the original buyers prior to the project completion. The estimation results are presented in Table 3. In Model 2, we control for time-varying effects by including a set of dummy variables for transaction year as well as unobserved fixed-effects associated with the homebuilder and location.17 Overall, the estimates in the model are significant with the models explaining more than 78% and 90% of the price variations in Model 1 and Model 2, respectively. All the explanatory variables behave as expected. The effect of living AREA and floor LEVEL on sales price is positive and robust across subsamples. Developments on freehold sites command a significant price premium, whilst age has a negative impact on sale price. As expected, the distance 16 The dominance of the presale market is consistent with the practice of selling new condominiums before their completion in Asia, such as China, Hong Kong, Taiwan, Singapore, South Korea, and Malaysia (see Munneke et al., 2011). It is noted that built-to-suit is not an option for multi-family developments. 17 The set of binary variables can usefully proxy unmeasurable attributes of the planning sectors and homebuilders that affect house prices, such as perception on the developers’ goodwill and reputation.

132

J.T.L. Ooi et al. / Journal of Housing Economics 26 (2014) 126–138

Table 2 Descriptive statistics of sample. Variable

Definition

Mean

Std. dev.

Min

Max

PRICE AREA AREA_SQ FLOOR FLOOR_SQ FH CONDO AGE CBD MRT NEWSALE SUBSALE CONQUAS

Transaction price Floor area of unit (m2) Squared floor area of unit Floor level of unit Squared floor level of unit Binary variable for freehold unit Binary variable for condominium unit No. of years between sale and completion Distance from CBD (km) Distance from MRT (km) Binary variable for new sale transaction Binary variable for sub sale transaction CONQUAS scores

1,086,317 122.35 16,848 9.99 174.54 0.37 0.85 1.78 9.06 1.38 0.55 0.12 83.30

987,611 43.35 18,310 8.65 352.70 0.48 0.35 3.15 4.90 1.16 0.50 0.32 5.96

107,000 26.00 676 1.00 1.00 0.00 0.00 0.00 0.37 0.06 0.00 0.00 62.50

30,000,000 1023.00 1,046,529 69.00 4761.00 1.00 1.00 14.00 17.91 6.52 1.00 1.00 96.20

This table presents the definition and sample statistics of the dependent and explanatory variables in the empirical models. The descriptive statistics are based on the final sample of 100,593 sale transactions between 1995 and 2011.

Table 3 Estimation results on the impact of construction quality. Model 1

Model 2

Model 3

Year dummy Location dummy Homebuilder dummy

12.10*** (679.2) 0.0106*** (70.68) 9.25e06*** (21.44) 0.00408*** (18.29) 4.55e05*** (7.592) 0.176*** (106.4) 0.0133*** (5.014) 0.0132*** (36.67) 0.0403*** (227.8) 0.00239*** (3.890) 0.0228*** (8.658) 0.0329*** (9.483) 0.00493*** (33.22) No No No

12.26*** (557.1) 0.00966*** (50.14) 9.51e06*** (16.88) 0.00527*** (27.34) 3.04e06 (0.549) 0.126*** (81.02) 0.0482*** (22.16) 0.0207*** (64.31) 0.0321*** (52.40) 0.00728*** (13.21) 0.0156*** (8.186) 0.0245*** (10.51) 0.00544*** (38.20) Yes Yes Yes

12.72*** (682.4) 0.00966*** (50.23) 9.50e06*** (16.90) 0.00532*** (27.66) 8.78e07 (0.159) 0.126*** (81.32) 0.0542*** (25.31) 0.0253*** (87.03) 0.0309*** (50.57) 0.00921*** (16.71) 0.00365* (1.912) 0.0199*** (8.496) 0.00438*** (28.90) Yes Yes Yes

Observations Adjusted R-squared

100,593 0.789

100,593 0.906

100,593 0.906

Constant AREA AREA_SQ LEVEL LEVEL_SQ FH CONDO AGE CBD MRT NEWSALE SUBSALE CONQUAS

This table presents the OLS estimation results with 1998 market-adjusted price as the dependent variable. In Model 1 and Model 2, the original CONQUAS scores are used. Model 3 uses the orthogonalized CONQUAS scores (which involves regressing the original scores against the development and market characteristics and using the residuals as the proxy for construction quality). White’s heteroscedastic robust t-statistic standard errors are reported in parenthesis. *** Represents significance at the 1.0% level. * Represents significance at the 10.0% level.

to CBD coefficient is negative and significant in all the models. The distance to metro station coefficient, however, is positive and significant, which suggests that the negative

externality from pedestrian congestion, noise, or other negative externalities from a metro station is stronger than the countervailing positive externality of easy access to mass

133

J.T.L. Ooi et al. / Journal of Housing Economics 26 (2014) 126–138 Table 4 Correlation of project characteristics with CONQUAS scores.

PRICE AREA FLOOR FH CONDO AGE CBD MRT CONQUAS CONQUAS#

PRICE

AREA

FLOOR

FH

CONDO

AGE

CBD

MRT

CONQUAS

1 0.6297 0.2375 0.3688 0.0403 0.0008 0.5002 0.0648 0.3091 0.0734

1 0.0163 0.1636 0.1401 0.0192 0.0489 0.0735 0.0149 0.0378

1 0.0548 0.1720 0.1008 0.3518 0.2460 0.2938 0.0468

1 0.1411 0.1390 0.1822 0.0724 0.1631 0.0136

1 0.0013 0.1478 0.0102 0.0160 0.0197

1 0.1495 0.0577 0.2502 0.0357

1 0.1968 0.4288 0.0378

1 0.1603 0.0685

1 0.6157

This table presents the pair-wise correlation coefficient between CONQUAS scores and the other explanatory variables in the empirical model based on the final sample of 100,593 observations. The definition of the variables is presented earlier in Table 2. CONQUAS# represent the orthogonalized CONQUAS scores, which are the residuals from regressing the actual CONQUAS scores against the development’s characteristics.

transportation (see Munneke et al., 2011).18 The underlying performance of the real estate market also has a strong impact on transaction prices. Our main variable of interest is construction quality, which is instrumented by the project’s CONQUAS score. If construction quality is capitalized into house prices, then we would see a significant relationship between the CONQUAS score and price. The regression results indeed show that homes with higher CONQUAS score command a higher price, all else equal. Economically, the magnitude of the coefficient indicates an increase of 0.49% in the selling price for each additional CONQUAS score. In other words, a one standard deviation increase in the CONQUAS score would result in the average apartment in our sample selling for 2.92% higher, or S$31,720 more. For a large project with 500 units, the overall increase in the sales revenue arising from one standard deviation improvement in the CONQUAS score would be around S$ 15.9 million. While it may be argued that the observed premium may be related to either the developer’s reputation (Chau et al., 2007),19 it should be noted that the coefficient for CONQUAS continues to be significant after controlling for unobserved fixed-effects associated with individual homebuilders in Model 2. Table 4 reports the pairwise correlation matrix between CONQUAS scores and various attributes of the residential project. Consistent with regression results reported earlier, we see a significant positive relationship between the CONQUAS scores and average selling price of the project. However, the CONQUAS scores are also correlated with various attributes of the development and market. For example, we observe that expensive and freehold 18 The results are robust to measuring the distance to MTA and CBD in natural logarithm to account for non-linearity. As suggested by a referee, the positive sign on MTA could be less indicative of characteristics of the metro station than to those of the street under which the MTA lies. In particular, MTA was planned to improve access to certain neighborhood, and there is some path dependence of passenger rail lines to pre-existing road system. Nevertheless, 36 out of the 53 MTA stations on the two service lines in Singapore are located above ground. 19 Comparing two residential developments in Hong Kong that are similar in most aspects except for their developers, Chau et al. (2007) find that the project built by the more reputable homebuilder commanded a 6.4% premium, which persisted even after the completion of both projects.

properties tend to have higher construction quality. Development height and distance to CBD also have significant relationship with construction quality. To mitigate the potential bias, we orthogonalize the CONQUAS scores by regressing the actual CONQUAS scores against a set of development characteristics, namely size, tenure, development height and a set of fixed effects for planning districts and time, and employing the residuals (which are then not related to the explanatory variables) as a proxy for construction quality. The regression results, presented under Model 3 that applies the orthogonalized CONQUAS, are consistent with our earlier conclusion regarding the relationships of the explanatory variables and sale price. In Table 5, we further substitute the overall CONQUAS score with the value of the respective components, namely structural, architectural, and M&E scores.20 The estimation results show that coefficient for each component score is positive and statistically significant. Overall, the observed positive relationship between sale price and construction quality is robust to alternative proxies used to measure the quality of construction. To examine the impact of construction quality in the different sub-markets, we also run separate regressions for transactions in the resale, sub-sale and presale housing markets. While the estimation results, as tabulated in Table 6, show that the relationship between house price and construction quality is robust, they also reveal several implications on the relationship between house price and construction. First, concerns over the quality of workmanship and construction defects are expected to be more pressing in the presale and sub-sale markets where dwelling units are purchased prior to their physical completion. Chau et al. (2007), for example, contend that housing units sold under a presale system suffer from the risk of having lower quality than intended in the initial contract because homebuilders, having received payment for the unfinished units, have incentives to increase their profit by cutting construction costs. Ong (1997) also postulates that building defects are accentuated by the practice of homebuild20 For this procedure, 593 transactions had to be omitted because the disaggregated CONQUAS scores of a development, namely Glendale Park, are not available. The regression results are not sensitive to the omission of this development.

134

J.T.L. Ooi et al. / Journal of Housing Economics 26 (2014) 126–138

Table 5 Estimation results with alternative measures of construction quality.

Constant AREA AREA_SQ LEVEL LEVEL_SQ FH CONDO AGE CBD MRT NEWSALE SUBSALE STRUCTURAL

Model 4

Model 5

Model 6

13.05*** (851.2) 0.00967*** (50.22) 9.50e06*** (16.89) 0.00547*** (28.35) 2.79e06 (0.504) 0.128*** (79.58) 0.0586*** (27.67) 0.0254*** (86.21) 0.0288*** (46.87) 0.00804*** (14.66) 0.00255 (1.327) 0.0185*** (7.855) 0.00284*** (22.39)

13.03*** (860.7) 0.00968*** (50.12) 9.52e06*** (16.88) 0.00536*** (27.79) 1.53e06 (0.276) 0.121*** (75.69) 0.0530*** (24.39) 0.0254*** (87.14) 0.0319*** (51.52) 0.00971*** (17.61) 0.00334* (1.740) 0.0193*** (8.213)

13.03*** (860.7) 0.00966*** (50.32) 9.47e06*** (16.90) 0.00528*** (27.46) 2.26e06 (0.410) 0.126*** (78.97) 0.0590*** (27.66) 0.0255*** (87.41) 0.0316*** (51.82) 0.00951*** (17.36) 0.00325* (1.690) 0.0199*** (8.419)

0.00282*** (24.49)

ARCHITECTURAL

Year dummy Location dummy Homebuilder dummy

Yes Yes Yes

Yes Yes Yes

0.00221*** (28.31) Yes Yes Yes

Observations Adjusted R-squared

100,000 0.906

100,000 0.906

100,000 0.906

M&E

This table presents the OLS estimation results with 1998 market-adjusted price as the dependent variable. In Model 4, Model 5 and Model 6, the overall CONQUAS score for each project is replaced by the three sub-components, namely structural, architectural and M&E works, respectively. White’s heteroscedastic robust t-statistic standard errors are reported in parenthesis. *** Represents significance at the 1.0% level. * Represents significance at the 10.0% level.

ers marketing their projects prior to the completion of construction. Thus, all else being equal, one would expect a lower premium attached to construction quality for homes that are sold before their physical completion. Model 9 in Table 6 indeed shows that buyers in the presale market are likely to discount the premium for construction quality. Second, while the possibility that house prices may influence CONQUAS scores in the presale market cannot be discounted,21 the fact that the coefficient for CONQUAS remains positive and statistically significant in the sample using only resale transactions shows that the positive premium attached to construction quality still persists after controlling for the potential endogenous variable. Moreover, to account for the predictability of the construction quality of homes under construction, we re-estimate the model for

21 Highlighting that maintenance may be endogenous to house value, Harding et al. (2007) argue that ‘‘it is likely that owners of expensive homes will spend more on home maintenance than owners of inexpensive homes. As such, one cannot simply put maintenance into a standard ordinary lease squares hedonic regression of house value.’’ (p. 196).

the presale sample using a two-stage regression: In the first stage, a predicted CONQUAS score is derived for each project in our sample using the score achieved for previous projects by the same developer.22 The predicted scores are then used as an instrument variable for construction quality in the second stage regression. The estimation results of the second stage regression are presented in the last column (Model 10) of Table 6. The results are consistently positive, indicating that construction quality is an important determinant of selling prices in the presale market. The evidence also indicates that a developer’s commitment to quality construction in a current project can be predicted from his previous projects. In sum, the regression results suggest that developers can extract a price premium directly on quality homes, and indirectly by gaining good reputation through a sequence of consistently high quality projects.

22 The results of the first stage regression are not reported because they are essentially similar to the correlation matrix (Table 4).

135

J.T.L. Ooi et al. / Journal of Housing Economics 26 (2014) 126–138 Table 6 Estimation results on the impact of construction quality (by sub-markets). Model 7 OLS

Model 8 OLS

Model 9 OLS

Model 10 IV

12.19*** (196.5) 0.0105*** (43.91) 1.01e05*** (14.90) 0.00558*** (12.47) 1.23e05 (1.323) 0.0647*** (11.87) 0.0486*** (8.825)

12.98*** (482.6) 0.00976*** (27.94) 9.64e06*** (9.382) 0.00739*** (27.29) 4.92e05*** (6.301) 0.160*** (80.78) 0.0672*** (22.99)

12.47*** (211.3) 0.00974*** (28.32) 9.29e06*** (9.306) 0.00642*** (22.37) 3.41e05*** (4.255) 0.149*** (62.72) 0.0847*** (20.56)

Year dummy Location dummy Homebuilder dummy

12.73*** (378.6) 0.00895*** (31.39) 9.02e06*** (10.70) 0.00504*** (16.24) 7.63e06 (0.771) 0.111*** (37.50) 0.0438*** (11.53) 0.0215*** (56.82) 0.0384*** (34.05) 0.0156*** (10.42) 0.00692*** (25.45) Yes Yes Yes

0.0252*** (12.10) 0.0126*** (4.063) 0.00607*** (12.09) Yes Yes Yes

0.0286*** (38.21) 0.0112*** (15.59) 0.00362*** (19.31) Yes Yes Yes

0.0274*** (32.80) 0.0137*** (15.38) 0.00224*** (3.831) Yes Yes Yes

Sub-market Observations Adjusted R-squared

Resale 33,499 0.909

Sub-sale 11,767 0.935

Presale 55,327 0.914

Presale 42,081 0.921

Constant AREA AREA_SQ LEVEL LEVEL_SQ FH CONDO AGE CBD MRT CONQUAS

Models 7, 8 and 9 report the OLS regression results for the sub-samples of resale, sub-sale, and new sale, respectively. In the first three models, the original scores are regressed against the development and market characteristics and the residuals are then used as the proxy for construction quality. Model 10 reports the IV estimation result for the sub-sample of new sales. The fitted values of CONQUAS in Model 10 are derived from the following equation CONQUASi,di,i = b CONQUASj,d,t–k + e + x, where CONQUASi,d,t is the CONQUAS score of project i by homebuilder d completed in quarter t; CONQUASj,p,t–k is the CONQUAS score of project j by the same homebuilder d completed in quarter t–k (k P 1, and k is the smallest possible number, such that j is the immediate project preceding i); e is a set of fixed effect for developer. Note that the number of projects is reduced to 160 projects because projects of the same homebuilders and completed in the same quarter are filtered out. White’s heteroscedastic robust t-statistic standard errors are reported in parenthesis. *** Represents significance at the 1.0% level.

Although the premium attached to good construction quality exists in all the sub-markets, Table 6 shows that the premium is highest in the resale market. This interesting result suggests that buyers who pay more for quality homes in the presale market can recoup the premium when they sell the units subsequently in the secondary market. To probe this further, we examine whether there is any significant difference in the investment performance of good construction quality homes. This line of enquiry is consistent with prior studies which found that houses do not appreciate at the same rate due to their property attributes, such as age, type and location (Smith and Tesarek, 1991; Archer et al., 1996; Smith and Ho, 1996; Clapp and Giaccotto, 1998; Coulson and Lahr, 2005; Harding et al., 2007; Bourassa et al., 2009; Ferreira and Gyourko, 2012).

5. Construction quality and capital appreciation We employ a generalized version of the standard repeat-sales model to estimate the house price appreciation for each pair of repeat sales in our sample. Archer et al. (1996), Harding et al. (2007) and Bourassa et al. (2009) employ a similar methodology to examine the

determinants of house price inflation over time. An advantage of using the repeat sales approach is that it removes any potential bias from unobserved apartment or neighborhood characteristics that remain unchanged over time. Specifically, the log-price change between two sale dates is regressed on the property’s CONQUAS scores and a set of control variables suggested by prior literature:

    Ptþi Mkttþi 2  ln ¼ a þ bi þ ci þ dPRIME ln pt Mktt þ lCONQUAS þ cPRESALE þ e

ð2Þ

where pt and pt+i are the transacted prices at time t and t + i respectively, and Mktt and Mktt+i are the market price indices at the corresponding time period. Thus, the dependent variable in Eq. (2) is the difference between the price change of a resale unit and the average price change in the market. We also include the number of quarters between the two sales, i, which captures age-related depreciation, as well as its square term to reflect the fact that houses depreciate at a non-linear rate (see Shilling et al., 1991; Lee et al., 2005). The binary variable PRIME is defined as units that are located in the prime residential district. Finally, we incorporate PRESALE which is a binary

136

J.T.L. Ooi et al. / Journal of Housing Economics 26 (2014) 126–138

Table 7 Descriptive statistics of repeat sales sample. Variable

Definition

Mean

Std. dev.

Min

Max

RETURN_PA i PRIME PRESALE CONQUAS

Market-adjusted house price change (annualized) Number of quarters between the two sales Binary variable for unit in prime residential location Binary variable for pairs of repeat sales in which the first sale involves a presale transaction CONQUAS score

0.03 19.62 0.15 0.66 82.89

0.16 14.21 0.35 0.47 5.52

0.80 0.00 0.00 0.00 67.40

4.15 63.00 1.00 1.00 95.60

This table reports the descriptive statistics for 23,843 pairs of repeat sales between 1998 and 2010. The holding period return for each pair is calculated as the percentage price changes between the first sale and the second sale, after adjusting for changes in the house price index over the corresponding period. Note that for the regression models, the holding period real returns are utilized. However, for reporting purpose, we report the annualized appreciation rate for ease of interpretation.

variable, representing pairs of repeat sales in which the first sale involves a presale transaction. This will allow us to monitor whether investors earn superior returns by buying new houses from homebuilders, or by buying existing properties in the secondary market. To the extent that homebuilders time their new project launches to take advantage of hot sentiment in the property market, we expect the appreciation rate to be lower for PRESALE properties.23 We are primarily interested in the coefficient l of CONQUAS, with the null hypothesis that the appreciation rates of dwelling units are invariant with respect to construction quality of the new homes; that is, prices of good quality homes will move in tandem with the overall market.24 In total, 23,843 pairs of repeat sales were identified over the sample period with each pair representing two consecutive sales of the same property. Table 7 presents the summary statistics for our sample of repeat sales. About 15% of the sample is located in prime residential areas. For those who resold their houses, the average holding period is approximately 5 years.25 The average market-adjusted capital appreciation of resale homes in our sample is 3% per annum, which is higher than the 7% registered by the aggregate housing market over the corresponding period.26 It should be noted that the mean figures are skewed by a few observations that occurred 23 An alternative specification is to include the market average price changes on the right-hand side of Eq. (2). The regression results are consistent. 24 Smith and Ho (1996) contend that in the long run, demand shocks trigger supply responses such that long-run real prices vary along the longrun supply curve. They argue that unless supply constraints (such as land use controls, building codes, and zoning regulations), or technological innovation (such as systems building or increased prefabrication) systematically favor one stratum of housing and thus generate differently sloped long-run supply curves for different valued housing, relative long-run real prices will be invariant with respect to value. 25 This is consistent with Harding et al. (2007) estimates of just below 6 years for US homeowners’ time in the home. They also find the median time between sales dates in their sample of 6841 repeat sales is 5 years. Splitting our sample into two sub-samples according to whether the property was brought directly from the developer in the primary market or from the original buyer in the secondary market, we interestingly observe that the average holding period of units in the first category is 5.9 years, as compared to only 3.1 years for units in the second category. 26 The nominal annual capital appreciation of resale homes in our sample is 10%, which is 3 percentage points higher than the 7% registered by the aggregate housing market over the corresponding period. As noted by Hendershott and Hu (1981), housing ownership is very much a leveraged investment, and as such, can lead to higher rates of return than the appreciation rates presented here. See also Pollakowski et al. (1991).

during the property boom in 2007. In particular, some houses were quickly turned over with nominal price increases of 30–60% within 3–6 months.27 The regression results are presented in Table 8. The first two models are run on the full sample of repeat sales using original (Model 11) and orthogonalized (Model 12) CONQUAS scores as proxies for quality. All the control variables also behaved as expected. For example the coefficient of age-related depreciation, as instrumented by the time between two sales, is negative, indicating that depreciation dampens price growth at a decreasing rate. Location also appears to be an important determinant of house price appreciation, as evidenced by the significant positive coefficient for PRIME. Specifically, prices of houses located in the prime residential districts rose faster than an otherwise similar property in non-prime districts. We also observe that houses bought directly from the homebuilders offer a lower return to investors as compared to existing houses that are bought in the secondary market. The lower return is consistent with the notion that homebuilders, due to superior information or stronger market power, are price leaders in the housing market (Glaeser et al. (2008) and Ooi and Le (2012, 2013).28 Focusing on the contribution of construction quality, we find positive and statistically significant coefficients for CONQUAS, indicating that good construction quality homes registered higher capital appreciation, after controlling for market condition, age and location of the properties. The magnitude of the coefficient implies that for every one point increase in the CONQUAS score, the capital appreciation of an average apartment over a five-year holding period would be $5279 more than the average appreciation of the whole market over the same period. A one standard deviation increase in CONQUAS score of 5.52, therefore, can improve the price appreciation of the average apartment by $29,143 over its holding period, which is typically 5 years. Overall, the relative investment returns of houses with different construction quality indicate a growing demand for houses with high workmanship quality. 27 Our regression results are robust to the exclusion of these 82 observations. 28 Examining the spillover effects of infill developments on local housing prices, Ooi and Le (2013) find a contagion effect which could be traced to developers overpricing the new homes by 4.8% on average. In contrast, the resale housing market is dominated by ‘‘amateurs’’, who trades infrequently and have less up-to-date information (see also Glaeser and Nathanson, 2014).

137

J.T.L. Ooi et al. / Journal of Housing Economics 26 (2014) 126–138 Table 8 The effect of construction quality on house price appreciation.

Constant i i2 PRIME PRESALE CONQUAS Sample Observations R-squared

Model 11

Model 12

Model 13

Model 14

0.276*** (15.88) 0.00858*** (34.12) 8.48e05*** (18.82) 0.0373*** (11.79) 0.0374*** (15.67) 0.00486*** (23.91)

0.132*** (53.34) 0.00927*** (36.76) 8.68e05*** (19.11) 0.0478*** (15.52) 0.0296*** (12.43) 0.00260*** (8.889)

0.167*** (35.16) 0.0116*** (20.91) 0.000123*** (11.48) 0.0568*** (3.800) 0.0514*** (10.82) 0.00135** (2.423)

0.0642*** (13.03) 0.00288*** (5.774) 2.51e06 (0.265) 0.0368*** (8.599) 0.00561 (1.285) 0.00680*** (11.80)

All sales 23,843 0.236

All sales 23,843 0.219

Least expensive 6448 0.246

Most expensive 5750 0.10

Model 11 and Model 12 report the OLS regression for the full sample of repeat sales. Model 13 and Model 14 present the regression results for two subsamples of ‘‘least expensive’’ and ‘‘most expensive’’ homes, which represent the bottom and top 25% of all the resale homes. In all the models, the dependent variable is the log-price change of the house over the two sales dates, adjusted for market returns over the corresponding period. The right-hand side variables are the number of quarters between the two sales (i), the number of quarters between the two sales squared (i2), a binary variable for units located in prime residential districts (PRIME), and construction quality (CONQUAS). In Model 11, the original CONQUAS scores are employed. In Models 12, 13 and 14, the orthogonal CONQUAS scores are employed. This involves regressing the original scores against the development and market characteristics and using the residuals as the proxy for construction quality. White’s heteroscedastic robust t-statistic standard errors are reported in parenthesis. ** Represents significance at the 5.0% level. *** Represents significance at the 1.0% level.

The housing literature has also recorded differences in the appreciation rates of high and lower value homes (see Smith and Ho, 1996; Smith and Tesarek, 1991). We therefore turn our attention on the impact of construction quality on house price appreciation for two sub-samples of ‘‘least expensive’’ and ‘‘most expensive’’ houses, which is defined as homes in bottom 25 percentile and top 25 percentile based on their original sale price, respectively. The results, which are reported in Table 8 under Model 13 and Model 14, show that our earlier results are robust. More importantly, the positive impact of construction quality on price appreciation can still be seen in both sub-samples. Comparing the relative magnitude of the CONQUAS coefficients in the two models, the effect of construction quality is three times bigger for the sub-sample of ‘‘most expensive’’ houses. This implies that the marginal effect of improvements in construction quality on the rate of house price appreciation is larger for more expensive properties.

results also clearly suggest that the prices of houses that are well constructed in the beginning appreciate at a significantly higher rate than the prices for average quality house in the market. This is also empirically supported by our findings that the CONQUAS premium is highest in the resale market. Interestingly, we also observe that homebuyers in the presale and sub-sale markets are willing to pay for good construction quality even before the project’s completion. The combined results appear to suggest that although houses with good workmanship cost more to homebuyers, they also offer higher prospective returns in terms of capital appreciation to the investors. The significant premium and price appreciation associated with construction quality also imply that the housing market may not need government intervention to encourage the developers to build better housing. However, data on construction and maintenance cost are needed to empirically determine the elasticity of each effect.

6. Conclusion This study contributes to the literature by examining the relationship between construction quality and house prices. Using Singapore as our case study, we are able to circumvent most of the empirical problems that plague past studies and this paper serves as the first to explore the issue further. Our empirical analysis is based on a unique dataset comprising 100,593 sale transactions in 205 private residential developments completed in Singapore between 1998 and 2010 matched with the construction quality score for each development. We found that buyers pay a significant premium for good workmanship quality. The

References Akerlof, A.G., 1970. The market for ‘‘lemons’’: quality uncertainty and the market mechanism. Quart. J. Econ. 84 (3), 488–500. Archer, W.R., Gatzlaff, D.H., Ling, D.C., 1996. Measuring the importance of location in house price appreciation. J. Urban Econ. 40, 334–353. Baum, A.E., 1993. Quality, depreciation, and property performance. J. Real Estate Res. 8 (4), 541–565. Bourassa, S.C., Haurin, D.R., Haurin, J.L., Hoesli, M., Sun, J., 2009. House price changes and idiosyncratic risk: the impact of property characteristics. Real Estate Econ. 37 (2), 259–278. Brochner, J., 2011. Developing construction economics as industry economics. In: de Valence, G. (Ed.), Modern Construction Economics: Theory and Application. Spon Press, pp. 14–28. Campbell, J.Y., Giglio, S., Pathak, P., 2011. Forced sales and house prices. Am. Econ. Rev. 101 (5), 2108–2131.

138

J.T.L. Ooi et al. / Journal of Housing Economics 26 (2014) 126–138

Chau, K.W., Wong, S.K., Yiu, C.Y., 2007. Housing quality in the forward contracts market. J. Real Estate Finan. Econ. 34, 313–325. Chen, J., Rutherford, R.C., 2012. Quality and time-on-the-market in residential markets. J. Real Estate Finan. Econ. 44, 414–428. Clapp, J.M., Giaccotto, C., 1998. Price indices based on the hedonic repeatsales method: application to the housing market. J. Real Estate Finan. Econ. 16, 5–26. Coulson, N.E., Lahr, M.L., 2005. Gracing the land of Elvis and Beale Street: historic designation and property values in Memphis. Real Estate Econ. 33, 487–507. Englund, P., Hwang, M., Quigley, J.M., 2002. Hedging housing risk. J. Real Estate Finan. Econ. 24, 167–200. Ferreira, F., Gyourko, J., 2012. Heterogeneity in Neighbourhood-Level Price Growth in the United States, 1993–2009. Glaeser, E.L., Nathanson, C.G., 2014. Housing bubbles. NBER Working Paper Series No. 20426. Glaeser, E.L., Gyourko, J., Saiz, A., 2008. Housing supply and housing bubbles. J. Urban Econ. 64, 198–217. Gyourko, J., Linneman, P., 1990. Rent controls and rental housing quality: a note on the effects of New York City’s old controls. J. Urban Econ. 27, 398–409. Harding, J.P., Rosenthal, S.R., Sirmans, C.F., 2007. Depreciation of housing capital, maintenance, and house price inflation: estimates from a repeat sales model. J. Urban Econ. 61, 193–217. Hendershott, P.H., Hu, S.C., 1981. Inflation and extraordinary returns on owner occupied housing: some implications for capital allocation and productivity growth. J. Macroecon. 3, 177–203. Kain, J.F., Quigley, J.M., 1970. Measuring the value of housing quality. J. Am. Stat. Assoc. 65, 532–548. Kim, Y.W., Kim, S.C., 2011. Cost analysis of information technologyassisted quality inspection using activity-based costing. Constr. Manag. Econ. 29, 163–172. Le Blanc, D., Lagarenne, C., 2004. Owner-occupied housing and the composition of the household portfolio: the case of France. J. Real Estate Finan. Econ. 29, 259–275. Lee, B.S., Chung, E., Kim, Y.H., 2005. Dwelling age, redevelopment, and housing prices: the case of apartment complexes in Seoul. J. Real Estate Finan. Econ. 30, 55–80. Lichtenstein, L., Kern, C.R., 1987. The cost of quality in existing housing: estimates from an implicit markets model. J. Urban Econ. 22, 324– 339. Ling, F.Y.Y., 2005. Models for predicting quality of building projects. Eng. Constr. Architect. Manag. 12 (1), 6–20. Low, S.P., Yeo, H.K.C., 1998. A construction quality costs quantifying system for building industry. Int. J. Qual. Reliab. Manag. 15 (3), 329– 349.

Moon, C., Stotsky, J., 1993. The effect of rent control on housing quality change: a longitudinal analysis. J. Polit. Econ. 101, 1114–1148. Munneke, H.J., Ooi, J.T.L., Sirmans, C.F., Turnbull, G.K., 2011. Sequential sales of similar assets: the law of one price and real estate. J. Reg. Sci. 51 (2), 355–370. Oakland, Marosszeky, M., 2005. Total Quality Management in the Construction Supply Chain. Heinemann, Butterworth. Ofori, G., Gang, G., 2001. ISO 9000 certification of Singapore construction enterprises: its costs and benefits and its role in the development of the industry. Eng. Constr. Architect. Manag. 8 (2), 145–157. Olsen, E.O., 1988. What do economists know about the effect of rent control on housing maintenance? J. Real Estate Finan. Econ. 1, 295– 307. Ommeren, J.V., Koopman, M., 2011. Public housing and the value of apartment quality to households. Reg. Sci. Urban Econ. 41, 207–213. Ong, S.E., 1997. Building defects, warranties and project financing from pre-completion marketing. J. Property Finan. 8 (1), 35–51. Ooi, J.T.L., Le, T.T.T., 2012. New supply and price dynamics in the Singapore housing market. Urban Stud. 49, 1435–1451. Ooi, J.T.L., Le, T.T.T., 2013. The spillover effects of infill developments on local housing market. Reg. Sci. Urban Econ. 43, 850–861. Pollakowski, H.O., Stegman, M.A., Rohe, W., 1991. Rates of return on housing of low- and moderate-income owners. AREUEA J. 19 (3), 417– 425. Rosen, S., 1974. Hedonic prices and implicit markets: product differentiation in pure competition. J. Polit. Econ. 82 (1), 34–55. Rosenfeld, Y., 2009. Cost of quality versus cost of non-quality in construction: the crucial balance. Constr. Manag. Econ. 27, 107–117. Shilling, J.D., Sirmans, C.F., Dombrow, J.F., 1991. Measuring depreciation in single-family rental and owner-occupied housing. J. Hous. Econ. 1, 368–383. Sims, D.P., 2007. Out of control: what can we learn from the end of Massachusetts rent control? J. Urban Econ. 61, 129–151. Smith, L.B., Ho, M.H.C., 1996. The relative price differential between higher and lower priced homes. J. Hous. Econ. 5, 1–17. Smith, B.A., Tesarek, W.P., 1991. House prices and regional real estate cycles: market adjustments in Houston. AREUEA J. 19 (3), 396–416. Taylor, C.R., 1999. Time-on-the-market as a sign of quality. Rev. Econ. Stud. 66, 555–578. Vandell, K.D., Lane, J.S., 1989. The economics of architecture and urban design: some preliminary findings. AREUEA J. 17 (2), 235–260. Wieand, K.F., 1983. The performance of annual housing survey quality measures in explaining dwelling rentals in 20 metropolitan areas. AREUEA J. 11 (1), 45–66. Yasamis, F., Arditi, D., Mohammadi, J., 2002. Assessing contractor quality performance. Constr. Manag. Econ. 20, 211–223.