# RECORD 7a87d94cbb2a1bb3af93865c70eaef38f5783daa # TITLE: Hedonic Modeling for a Growing Housing Market: Valuation of Apartments in Complexes # YEAR: 2014.0 VENUE: nan DOI: 10.5539/ijef.v6n3p188 # AUTHORS: Ebubekir Ayan; H. C. Erkin # PAGES: 12 CANDIDATE TABLE PAGES (1-based): [5, 8] # ABSTRACT: The share of apartment complexes in housing stock of major cities in the developing world has been increasing. They represent a different housing submarket whose growing importance necessitates further research. Using survey data on transaction records of real estate agents, we employ a hedonic pricing model to explore the factors influencing apartment prices in the Metropolitan Izmit area. The site of a major reconstruction effort following a strong earthquake, Izmit has been in the forefront of the changes taking place in the Turkish real estate sector in particular, and in urban housing in general. We aim to get better estimates by focusing the study on apartment complexes in Metropolitan Izmit, the major submarket in the area. The results reveal that structural characteristics of an apartment as well as amenities provided in an apartment complex are important determinants of price. Air quality and proximity to a good public school have a major effect on price as well. The findings suggest that the submarket could be segmented further geographically. ===== PAGE 1 (score 14, numeric-paren 0, table-words 0, var-words 14) ===== International Journal of Economics and Finance; Vol. 6, No. 3; 2014 ISSN 1916-971X E-ISSN 1916-9728 Published by Canadian Center of Science and Education 188 Hedonic Modeling for a Growing Housing Market: Valuation of Apartments in Complexes Ebubekir Ayan1 & H. Cenk Erkin2 1 Marmara University, Turkey 2 Kocaeli University, Turkey Correspondence: Ebubekir Ayan, Marmara University, Nisantasi Campus, 34365 Istanbul, Turkey. Tel: 212-233-0447. E-mail: bekir.ayan@marmara.edu.tr Received: December 7, 2013 Accepted: December 20, 2013 Online Published: February 25, 2014 doi:10.5539/ijef.v6n3p188 URL: http://dx.doi.org/10.5539/ijef.v6n3p188 Abstract The share of apartment complexes in housing stock of major cities in the developing world has been increasing. They represent a different housing submarket whose growing importance necessitates further research. Using survey data on transaction records of real estate agents, we employ a hedonic pricing model to explore the factors influencing apartment prices in the Metropolitan Izmit area. The site of a major reconstruction effort following a strong earthquake, Izmit has been in the forefront of the changes taking place in the Turkish real estate sector in particular, and in urban housing in general. We aim to get better estimates by focusing the study on apartment complexes in Metropolitan Izmit, the major submarket in the area. The results reveal that structural characteristics of an apartment as well as amenities provided in an apartment complex are important determinants of price. Air quality and proximity to a good public school have a major effect on price as well. The findings suggest that the submarket could be segmented further geographically. Keywords: real estate, asset pricing, housing prices, hedonic model, Turkey 1. Introduction Rapid urbanization has been a major factor for housing demand in developing countries since the middle of the last century. Urban population in those countries has increased by more than six fold during this period (Pugh, 2001). A major concern of policymakers has been the shortage of healthy and affordable living space in growing cities. City dwellers in better-off developing countries have been luckier in this respect; rising personal incomes and relatively well-functioning real estate and credit markets have allowed them to live in modern units with higher standards. These units are usually apartments, and increasingly, they are being located in a complex of multistoried buildings (Wester et al., 2002; Rabenhorst & Ignatova, 2009; Rosen & Walks, 2013). Apartment complexes offer several advantages over single-buildings for both developers and residents. Firstly, developers who build many units enjoy economies of scale because of lower input costs, better utilization of resources and employment of capital intensive methods. Their efficiency in construction, and thus low unit cost, allows them to set apartment prices lower compared to their smaller rivals. Secondly, offering buyers a variety of apartment types, layouts and quality becomes less costly as the size of the project increases. Thirdly, limited available land in an urban area makes provision of recreational facilities expensive for developers. Due to much lower cost per unit, large apartment complexes can have swimming pools, club houses and larger green space compared to single-buildings. Lastly, providers of public transportation and infrastructure services are more sensitive to the needs of a large population living in a complex probably because of both the larger demand and more political power (Ho, 2003). Living in apartment complexes has been gaining popularity in major Turkish cities. This trend has gained momentum as modernization of the urban housing stock has become a policy priority after the 7.4 magnitude earthquake in 1999. The disaster, in which more than 30 thousand buildings became unusable, revealed weaknesses of the aging housing stock. Government responded with the initiation of large housing projects. These early public initiatives, which were mainly oriented towards the victims of the earthquake, gained a higher momentum by the establishment of the Housing Development Administration of Turkey (TOKI) in 2002. TOKI, with its mission of providing modern and affordable dwellings has become a big player in the supply side of the ===== PAGE 5 (score 39, numeric-paren 15, table-words 1, var-words 22) ===== www.ccsenet.org/ijef International Journal of Economics and Finance Vol. 6, No. 3; 2014 192 Existence of additional bathrooms is consistently found to increase the value of a unit. In their survey article, Sirmans et al. (2005) report that in 40 studies where a variable for multiple bathrooms appeared, it had the expected sign in 39 of them, and was significant in 34. The binary variable BATHROOM takes value of 1 if an apartment has more than one bathroom. In the dataset, 56 apartments have two bathrooms and only one has three. Table 1. Descriptive statistics of data set (N = 405) Variable Description Mean (Std. Dev.) PRICE The sale price of the apartment (TL) 107216 (44847) AREA The floor area of the apartment (m2) 111.22 (27.55) AREA2 The square of the floor area of the apartment (m2) 13128 (7420) CNSTR_LQ Binary: the construction quality is below average 0.16 (0.37) AGE The age of the apartment building (years) 7.14 (5.21) BATHROOM Binary: there is more than 1 bathroom 0.14 (0.35) BLW_GRND Binary: the apartment is located below the ground floor 0.02 (0.13) GROUND Binary: the apartment is located on the ground floor 0.25 (0.43) NRTH_WST Binary: the apartment faces north and/or west 0.49 (0.50) POOL Binary: there is a swimming pool in the apartment complex 0.06 (0.24) SECURITY Binary: security guards are present in the apartment complex 0.15 (0.36) DISTANCE Binary: the distance from the city center (kilometers) 5.77 (3.56) YHYKPTN Binary: the apartment is in Yahyakaptan apartment complex 0.11 (0.31) SCHOOL Binary: there is a high quality public school within 1 kilometer 0.25 (0.43) AIR Binary: air is clean in the neighborhood 0.32 (0.47) KARTEPE Binary: the apartment building is located in Kartepe 0.18 (0.38) BASISKELE Binary: the apartment building is located in Basiskele 0.07 (0.25) There is considerable variation in construction quality, especially for apartment interiors, between the complexes in our data set. Even though the buildings are considered to be safe in the sense that they can withstand to a high magnitude earthquake similar to the one in 1999, the quality of materials used, such as roofing materials, heat and sound insulation, paint, floor coverings, kitchen countertops, wall tiles and toilet in bathroom, as well as the workmanship might differ significantly. Most of the apartments with poor quality interiors are located in complexes built by private contractors commissioned by the state enterprise TOKI. We designated a binary variable (CNSTR_LQ) that takes value of 0 if real estate agents consider interior construction quality of an apartment to be low. Metropolitan Izmit is located on a plain and the hills around it. Some apartments in a building located on a hill could be below the ground level. Such apartments are sold at a major discount compared to those above the ground level in the same building because they have low sunlight and no scenery. Ground level apartments also carry a price penalty, although not as much as those below them, because of limited view and noise created by people coming in and going out of the building. Binary variables BLW_GRND and GROUND are included to measure these price impacts. An apartment facing only north has lower sunlight and requires more energy to heat during colder months. North-side apartments could be less preferred because of health and heating cost concerns. Since apartment buildings in complexes are not attached like those in downtown, there is no apartment in the data set that faces only north. Therefore, we have created a binary variable (NRTH_WST) that takes a value of 1 if the apartment faces north and west. Demand for private residential security services in Metropolitan Izmit has been rising with the increasing number of gated communities. Although crime rate for the area is not above those of similar-sized cities, residential security services, probably signaling prestige and social rank, are seen as a selling point by developers ===== PAGE 6 (score 32, numeric-paren 0, table-words 1, var-words 30) ===== www.ccsenet.org/ijef International Journal of Economics and Finance Vol. 6, No. 3; 2014 193 and used in their marketing. Another amenity that is featured prominently in developer ads is swimming pools. Previous studies found that existence of a swimming pool increases the value of apartments in a complex (Sirmans et al., 2005). Therefore, the binary variables SECURITY and POOL are also included in the analysis. We take as the central business district the area around Anıtpark in downtown Izmit. It is the most visited site in the city due to government agencies, banks, a large number of stores, cafes and restaurants as well as the largest weekly one-day market located there. The variable DISTANCE measures how far in kilometers a particular apartment complex is from the central business district. A good education is seen as the key to a good job, and thus access to quality schools is very competitive in Turkey. Students who have completed 8th grade are first allocated to high-schools and then to universities according to their scores on nationwide centralized exams. A student's success in the university entrance exam is thought to be largely dependent on the high school she graduated from, and, in turn, her allocation to a high school to be dependent on the quality of her primary education. Access to public primary school is decided according to the location of the family. Parents either have to send their children to a public school in their school district or they have to choose a private school. The second option is only available to a minority since tuition and other expenses of private primary education could easily add up to half of the mean annual family income, about $12000 in 2012. On the other hand, there aren’t many high quality public primary schools given their performance at the high school entrance exam, SBS. Of 85 public primary schools in Izmit, only 6 was in the top 10% and 24 in the top 100 among 350 primary schools in Kocaeli according to a province-wide SBS trial test in 2012. Average test scores of high performing schools were nearly 30% higher than other schools’. Families with school aged children are known to move to neighborhoods with quality public schools so that their children can be enrolled to these schools. This could drive up the rents and apartment prices around quality public primary schools. We have chosen a public primary school’s performance in 2012 SBS trial test as a proxy for its educational quality (whether it was among the top 100 schools in Kocaeli). Since we don’t have data about school district boundaries, we created a binary variable (SCHOOL) that takes 1 if an apartment is within 1 km distance from a good public primary school. This definition of a school district is conservative; the implied school districts will probably be smaller than real ones. The effect of air quality on price is consistently found to be negative, though some studies reported the effect to be small (Smith et al., 1995). Later studies with more robust econometric methods found that air pollution had a large negative effect on residential unit prices (Zabel & Kiel, 2000; Chay & Greenstone, 2005). As an industrial center, Metropolitan Izmit is home to many factories producing a large variety of commodities ranging from chemicals, steel, machines, cars to construction materials. As it is also a major transportation hub, one of the most heavily used routes in Turkey passes through the city. Therefore, people living in the area have increasingly been concerned about the levels and possible effects of air pollution. Lacking an objective factor to measure air quality, we use a binary variable, AIR, to reflect real estate agents’ perceptions of air cleanness in a neighborhood. Chasco and Gallo (2013) show subjective evaluation measures of air quality can effectively explain part of the variation in housing prices. Apartments in Yahyakaptan complex, the largest and oldest in Metropolitan Izmit, are priced higher than apartments of similar quality in other complexes. The community has a higher proportion of professionals and well educated, upper middle class, white color workers (public servants, university employees, engineers, doctors and teachers). It has a theater, a shopping center and various cafes and restaurants, and is richer in green space and recreational opportunities compared to other large complexes. Although all apartment complexes have access to public transportation, Yahyakaptan is better connected to every major community in the area. Omitting a variable for the complex could create an upward bias for older complexes, thus we include the binary variable YHYKPTN in the estimation. Kartepe and Basiskele differ from Izmit in several aspects. Both municipalities have lower population density and limited service sector activity compared to Izmit. Until recently, agriculture has been the main economic activity but is being replaced with service jobs in Izmit and industrial jobs in the two municipalities. As a result, there is abundance of land available for development, especially in Kartepe, which is reflected in lower land values. In order to account for the probability that Metropolitan Izmit area has geographical submarkets as well, we include the binary variables KARTEPE and BASISKELE in the analysis. We prefer this method instead of dividing data into subsamples and performing F tests for coefficient equality because of the small size of our dataset. The variables could also proxy for some of location specific missing variables (those highly correlated with the municipality variables) in the model. We expect the coefficients for AREA, BATHROOM, POOL, SECURITY, YHYKPTN, SCHOOL, AIR to be ===== PAGE 8 (score 119, numeric-paren 101, table-words 5, var-words 8) ===== www.ccsenet.org/ijef International Journal of Economics and Finance Vol. 6, No. 3; 2014 195 All variables in models (1)–(4) have the expected sign and most are highly significant. Successive improvement from the base to the full model is observed with t values remaining significant, R2 increasing and both Akaike (AIC) and Bayesian (BIC) information criterion values consistently dropping (Note 3). The floor area is the variable with the most explanatory power; regressing the log of price on the floor area and its square alone explains 61% of the variation around the mean. Both AREA and AREA2 are consistently significant through the models with little change in their coefficients. Cost of construction is highly correlated with floor area; the availability of land for development especially in Kartepe and Basiskele could be a factor in keeping apartment prices close to construction costs. The relationship between the log price and the floor area is nonlinear as might be expected: % increase in price drops as area increases. In the full model, an additional m2 increases price by 0.84% at 80 m2 but only by 0.26% at 200 m2. When calculated at respective mean prices, the elasticity of price with respect to floor area is relatively constant around 0.7 up to 150 m2 and then drops to 0.5 around 200 m2. Apartments with poor build quality are penalized on average by a 10% drop in price. A similar finding, albeit conceptualized in the opposite way, is reported for Izmir: an above-average build quality increases the price by 15.8% (Yankaya & Celik, 2005). An apartment on the ground floor is discounted by 8.4% compared to apartments on upper floors in the same building. The discount reaches up to 23% for apartments below the ground level. Apartments facing northwest might be slightly less preferred, even though the effect is marginally significant only in the full model. Table 2. Hedonic price estimates. dependent variable: log (PRICE) (1) (2) (3) (4) CONSTANT 10.563*** (0.079) 10.599*** (0.081) 10.692*** (0.074) 10.610*** (0.072) AREA 0.012*** (0.001) 0.012*** (0.001) 0.012*** (0.001) 0.012*** (0.001) AREA2 -2.39E-05*** (0.47E-05) -2.45E-05*** (0.49E-05) -2.10E-05*** (0.38E-05) -2.42E-05*** (0.37E-05) CNSTR_LQ -0.140*** (0.021) -0.104*** (0.023) -0.104*** (0.025) -0.102*** (0.021) AGE -0.001 (0.001) -0.009*** (0.002) -0.018*** (0.002) -0.018*** (0.002) BATHROOM 0.135*** (0.030) 0.144*** (0.030) 0.123*** (0.033) 0.151*** (0.030) BLW_GRND -0.205*** (0.050) -0.191*** (0.045) -0.237*** (0.057) -0.266*** (0.051) GROUND -0.075*** (0.016) -0.078*** (0.016) -0.086*** (0.015) -0.088*** (0.014) NRTH_WST -0.011 (0.014) -0.013 (0.014) -0.014 (0.012) -0.019* (0.011) POOL 0.233*** (0.045) 0.234*** (0.046) 0.217*** (0.039) 0.190*** (0.041) SECURITY 0.196*** (0.023) 0.174*** (0.024) 0.134*** (0.024) 0.148*** (0.024) DISTANCE -0.018*** (0.003) -0.018*** (0.003) -0.030*** (0.003) -0.010** (0.005) YHYKPTN 0.158*** (0.029) 0.100*** (0.025) 0.134*** (0.024) SCHOOL 0.200*** (0.029) 0.151*** (0.028) AIR 0.141*** (0.020) 0.091*** (0.022) KARTEPE -0.185*** (0.032) BASISKELE -0.041 (0.072) R2 0.851 0.861 0.894 0.908 N 405 405 405 405 Note: White-Eicker heteroscedasticity robust standard error estimates in parentheses. Significant at 10% (*), at 5% (**), at 1% (***). Apartments having a second bathroom sell at a large premium, 16.3%; the effect is close to Tekel and Akbarishahabi (2013)’s estimate for apartments near Botanik Park in Ankara, 17.9%. Similarly, security on site and having access to a swimming pool increases the price on average by 16% and 20.9%, respectively. Selim’s (2008) estimate for swimming pool is more than 3 times larger, 54%, probably due to the heterogeneity in her nationwide sample where shanty houses are grouped with luxury apartments. Caglayan and Arikan (2011) find ===== PAGE 9 (score 18, numeric-paren 0, table-words 3, var-words 12) ===== www.ccsenet.org/ijef International Journal of Economics and Finance Vol. 6, No. 3; 2014 196 for Istanbul that security adds on price a premium of 16–22% but this might be an overestimate since pool is not included in their estimation. Even though the estimates reported here are lower, there could still be overestimation to some extent. These features are most common to more luxurious apartment complexes with high build quality and upper social class environment, both of which are not captured in our data. Distance to the central business district has a significant negative effect on price. The effect decreases significantly with the inclusion of municipality variables because apartments in Kartepe and Basiskele are on average 11.0 and 10.9 kms away from the center while those in Izmit are only 4.1 kms away. Nevertheless, the penalty for being away from the central business district in the full model is not marginal; a 5-km distance decreases the price by 5%. Proximity to a good public primary school and thus the option to enroll one’s child in there has a large impact on price, 16.3%. Our definition of proximity to being within 1 km distance from the school might slightly overestimate the average effect for the whole school district. Apartments farther away from the school but still in the same district could be priced less because parents living in those apartments would incur additional transportation costs in sending their children to the school. Haurin and Brasington (1996) and Downes and Zabel (2002) find the elasticity of house price with respect to school test score between 0.5 and 1 for US: a 10% increase in the average test score of a school raises house prices in the neighborhood by 5 to 10%. Zheng and Kahn (2008) report for Beijing that cutting distance to a core high school by half increases home prices by 7%. Table 3. Transformed regression coefficients (percentage change in price) AREA (at 100 m2) 0.007 CNSTR_LQ -0,097 AGE -0,018 BATHROOM 0,163 BLW_GRND -0,234 GROUND -0,084 NRTH_WST -0,019 POOL 0,209 SECURITY 0,160 DISTANCE -0,010 YHYKPTN 0,143 SCHOOL 0,163 AIR 0,095 KARTEPE -0,169 BASISKELE -0,041 Air quality is another neighborhood factor capitalized in apartment prices; apartments in areas perceived to have clean air sell at a premium of 9.5%. The stronger effect in (3) is reduced with the inclusion of Kartepe and especially Basiskele, the municipalities where air quality is generally superior. AGE has the expected negative sign but is statistically significant only after the effect of Yahyakaptan complex is controlled in models (2)–(4). Yahyakaptan buildings are the oldest in the data, but the apartments there sell at a large premium, around 14%, thanks to a unique set of favorable factors explained above. The increase in the adverse impact of age on price from 0.9 % in model 2 to 1.8 % in models 3 and 4 could be largely due to addition AIR to the specification because older projects, due to their location on the hills around the downtown, tend to have cleaner air. The results point to the existence of possible geographical submarkets in Metropolitan Izmit area. Kartepe has a large negative coefficient which is highly significant. Apartments in Kartepe are valued on average 16.9% less than those in Izmit. The coefficient for Basiskele is also negative but the hypothesis that it is not different from 0 cannot be rejected even at 10% level. A similar apartment can be built and sold for less in Kartepe compared to one in Izmit because the availability of large tracts of undeveloped agricultural land in Kartepe results in lower land prices and lower construction costs, which must be reflected in apartment prices there. Another factor pulling down the prices in the suburbs could be the capitalization of transportation costs: many people living in Kartepe and Basiskele have to commute daily to their jobs in Izmit. To our knowledge, this set of results is the first to be reported for Izmit. Our modeling strategy, focusing on the