# RECORD 281b355bd08bd1b8b228829d9c97b819d0d2a4d1 # TITLE: IMPACT OF AIR POLLUTION ON PROPERTY VALUES : A HEDONIC PRICE STUDY # YEAR: 2013.0 VENUE: nan DOI: 10.23917/jep.v14i1.150 # AUTHORS: Endah Saptutyningsih # PAGES: 14 CANDIDATE TABLE PAGES (1-based): [9, 10] # ABSTRACT: The main purpose of this study is the calculation of implicit prices of the environmental level of air quality in Yogyakarta on the basis of housing property prices. By means of Geographical Information System, the housing property prices characterized from the area which have highest air pollution level in province of Yogyakarta. Carbon monoxide is used as the pollution variable. The methodological framework for estimation is based on a hedonic price model. This approach establishes a relationship between the price of a marketable good (e.g. housing) and the amenities and characteristics this good contains. Therefore, if variations in air pollution levels occur, then households would change their behavior in an economic way by offering more money for housing located in highly improved environmental areas. The hedonic regression results that the housing price decrease while increasing the level of air contamination such substance as carbon monoxide. ===== PAGE 1 (score 6, numeric-paren 0, table-words 0, var-words 6) ===== Jurnal Ekonomi Pembangunan Volume 14, Nomor 1, Juni 2013, hlm. 52-65 IMPACT OF AIR POLLUTION ON PROPERTY VALUES : A HEDONIC PRICE STUDY Endah Saptutyningsih Fakultas Ekonomi Universitas Muhammadiyah Yogyakarta Jalan Lingkar Selatan, Tamantirto, Kasihan, Bantul, Yogyakarta E-mail: end_naufal@yahoo.com Diterima 30 Februari 2013 / Disetujui 4 April 2013 Abstract: The main purpose of this study is the calculation of implicit prices of the environ- mental level of air quality in Yogyakarta on the basis of housing property prices. By means of Geographical Information System, the housing property prices characterized from the area which have highest air pollution level in province of Yogyakarta. Carbon monoxide is used as the pollution variable. The methodological framework for estimation is based on a hedonic price model. This approach establishes a relationship between the price of a marketable good (e.g. housing) and the amenities and characteristics this good contains. Therefore, if variations in air pollution levels occur, then households would change their behavior in an economic way by offering more money for housing located in highly improved environmental areas. The hedonic regression results that the housing price decrease while increasing the level of air con- tamination such substance as carbon monoxide. Keywords: air pollution, hedonic price, property value, marginal willingness to pay curve JEL: Q53 Abstrak: Tujuan utama studi ini menghitung harga implisit dari tingkat kualitas lingkungan udara di Yogyakarta dengan berbasis pada harga properti perumahan. Dengan menggunakan sistem informasi geografis, harga properti perumahan dicirikan dengan daerah yang memiliki tingkat polusi udara tertinggi di provinsi Yogyakarta. Karbon monoksida digunakan sebagai variabel polusi. Kerangka metodologis untuk estimasi didasarkan pada model harga hedonis. Pendekatan ini menetapkan hubungan antara harga barang yang dipasarkan (misalnya perumahan) dan fasilitas dan karakteristik barang. Oleh karena itu, jika terjadi variasi dalam tingkat polusi udara, maka rumah tangga akan mengubah perilaku mereka secara ekonomi dengan menawarkan lebih banyak uang untuk perumahan terletak di daerah lingkungan yang baik. Hasil regresi hedonis menunjukkan bahwa penurunan harga perumahan akan menaik- kan tingkat pencemaran udara seperti misalnya karbon monoksida. Kata kunci: polusi udara, harga hedonis, nilai properti, kurva kesediaan marjinal membayar JEL: Q53 INTRODUCTION Urban pollution is a major focal point of public health concern and regularity activity (WHO, 2002). Pertinent guidelines and effective inter- ferences require knowledge of the burden of sickness and premature deaths attributable to specified pollutants. A long-term worldwide research program is focusing on effects of par- ticulates and other criteria pollutants such as NO2, O3 and CO (Brunekreef and Holgate, 2002). Among several tribes of valuation meth- ods, the hedonic price technique has been widely used to assess the effect of urban air pollution on property values in developed urban centers. Unfortunately, applications ad- dressed for developing urban centers are rare, and very little known about the effect of air ===== PAGE 9 (score 44, numeric-paren 20, table-words 7, var-words 10) ===== Jurnal Ekonomi Pembangunan Volume 14, Nomor 1, Juni 2013: 52-65 60 ronmental characteristic CO and the property price. All the structural parameters included in the model, like plot area and building area, are expected to have positive relations with the property price. Neighborhood characteristics like distance from school and hospital are inversely related with property price. It is normally expected that as distance from hospital and school increases, property price decrease, where as distance from city increases property price also increases. Applying these assumptions on the model specified above, the parameters are estimated using the method of ordinary least squares and is given in Table 4. Table 4. Regression Result of Hedonic Price Function Variable Full Model Fit Model (Constant) 3.323 (3.509)*** 2.783 (3.178)*** LnLANDSIZE 0.553 (7.840)*** 0.528 (7.658)*** LnBUILDSIZE 0.113 (1.380) 0.164 (2.093)** LnNUMROOM 0.094 (1.179) - WSTRUCTDUM -0.067 (-1.088) - LnDISSCHOOL -0.071 (-1.809)* -0.068 (-1.910)** LnDISHPITAL -0.085 (-1.980)** -0.075 (-1.885)* LnDISSPMARKET 0.023 (0.560) - LnDISREST -0.023 (-0.652) - LnDISCITY 0.054 (1.274) 0.064 (1.538) LnDISMSTREET 0.024 (1.030) - GARDENDUM 0.129 (1.673)* - LnCO -1.305 (-13.115)*** -1.375 (-15.066)*** No. Observation Adjusted R2 250 0.766 250 0.763 The main inferences of this analysis are given below: (1) All the neighborhood and structural char- acteristics except building size, wall structure, number of rooms, distance from supermarket, distance from restaurant, distance from city, and distance from main street are statistically significant at 5 percent level of significance. (2) The environmental characteristic, CO, is negatively related to house price and it signifi- cant at 1 percent level. The results also confirm that as the level CO increases by one percent house price reduces by 1.375 percent on the average. (3) Among the neighborhood characteristics, the distance from city positively related to property price. It showing that when distance increases, property price also increases. (4) Distance from school or hospital are nega- tively related to house price, showing that the plots nearer the school or hospital have high property values. (5) The plot area and bulding area are also positively related to property price. The regression results of the hedonic price function shows that all the significant estimated variables follow the expected relationship pat- tern. Hence the estimated equation could be written as: ln PPRICE = 2.783 + 0.528 lnLANDSIZE + 0.164 ln BUILDSIZE – 0.068 ln DISSCHOOL – 0.075 ln DISHPITAL – 1.375 ln CO + e Calculation of Implicit Marginal Price The first derivative of the hedonic price function can be interpreted as the implicit marginal price function for the environmental goods. Descriptive statistics of implicit marginal prices for 250 observations is given in the table 5. Table 5. Descriptive statistics of Implicit Marginal Price Descriptive Statistics Implicit Marginal Price (Rupiahs) Mean 20.592,0 Standard error 1.133,2 Median 15.147,9 Mode 13.200,0 Standard deviation 17.917,4 Minimum 1.555,7 Maximum 141.428,5 ===== PAGE 10 (score 42, numeric-paren 24, table-words 4, var-words 10) ===== Impact of Air Pollution (Endah Saptutyningsih) 61 Hence the marginal implicit price for reducing CO is calculated as Rp20.600,00. This result clearly identifies air quality as an important factor, along with structural and neighbourhood characteristics, in determining demand for property transaction in Daerah Istimewa Yogyakarta. Implicit Demand Function As mentioned earlier, second stage estimation of inverse demand curve is conducted by regressing the implicit marginal price on the quantity of environmental goods purchased and other socio economic features including income level of the individuals. The results are given in table 6. Table 6. Estimation of Inverse Demand Function Variable Full Model Fit Model (Constant) 3.449 (3.617)*** 3.022 (3.425)*** INCOMELEVEL 0.097 (1.515) 0.079 (1.351) LnFAMEMBER -0.079 (-0.939) - LnLANDSIZE 0.551 (7.819)*** 0.533 (7.727)*** LnBUILDSIZE 0.104 (1.266) 0.147 (1.863)* WALLSTRUCTDUM -0.062 (-0.998) - LnNUMROOM 0.083 (1.015) - LnDISSCHOOL -0.065 (-1.640) -0.067 (-1.889)* LnDISHOSPITAL -0.085 (-1.972)** -0.080 (-2.026)** LnDISSUPERMARKET 0.024 (0.584) - LnDISRESTAURANT -0.024 (-0.685) - LnDISCITY 0.056 (1.337) 0.071 (1.722)* LnDISMAINSTREET 0.031 (1.292) - GARDENDUM 0.108 (1.385) 0.177 (1.554) LnCO -0.317 (-3.156)*** -0.349 (-3.766)*** No. Observation Adjusted R2 250 0.651 250 0.646 The major inferences are: (1) The first derivative of the implicit marginal price function with respect to CO is negative (- 0.349) signaling decreasing marginal implicit prices for increasing environmental quality. It means that in the study area, a reduction in CO by one percent leads to 0.349 percent increase in property values. (2) The coefficient of number of family mem- bers, wall structure, distance from supermarket, and distance from main street are not significant. (3) All other variables except number of family members, wall structure, distance from supermarket, and distance from main street are significant at 5 percent level of significance. It is interesting to note that the distance from city increases by one percent, residential property value enhances by 0,071 percent. (4) Plot area and building area are positively related to residential property values. So far we have explained the hedonic price function estimation and its implications to residential property values. The results indicate very clearly that the households are willing to pay for improved air quality. It is therefore necessary to estimate the welfare benefits accruing to them trough the purchase of property with reduced air quality. CONCLUSION The primary objective of this study was to establish the relationship between air quality and the residential property values di province of Daerah Istimewa Yogyakarta. By mapping the area which has highest concentration of CO earlier and estimating the hedonic property value in the area, we can establish the relation- ship between air quality and the residential property values. In the model specification, we incorporated a number of structural, neighborhood, envi- ronmental and socioeconomic variables as determinants of consumer’s willingness to pay for reduced air quality. We hypothesized that the major environmental variable CO is inversely related to the residential property values. Similarly, the presence of garden, dis- tance from main street, distance from ===== PAGE 11 (score 14, numeric-paren 0, table-words 0, var-words 14) ===== Jurnal Ekonomi Pembangunan Volume 14, Nomor 1, Juni 2013: 52-65 62 supermarket, plot area, and building area are positively related to property price while distance from hospital and distance from restaurant are negatively related. By adopting a two-stage estimation proce- dure to estimate these relationships, we found that, on the average, an increase in the level of CO reduced property prices in the study area by 1.375 percent. We estimated the marginal implicit price for reducing CO as Rp 20.600,00. Estimates further revealed that the households are willing to pay an additional amount of 0.35 percent for a reduction in CO. In short, the analysis revealed a positive response of house- holds in Daerah Istimewa Yogyakarta -which have highest concentration of CO - between air quality and property prices. REFERENCES Bajari, P and CL Benkard. 2005. “Demand Esti- mation with Heterogeneous Consumers and Unobserved Product Characteristics: A Hedonic Approach”. Journal of Political Economy, 113(6), 1239-1276. Bhattacharya, Rabindra, N. 2002. Environmen- tal Economics: An Indian Perspective. New Delhi: Oxford University Press. Boyle, M. and K. Kiel. 2001. “A Survey of House Price Hedonic Studies of the Impact of Environmental Externalities”. Journal of Real Estate Literature. Vol. 9, No 2, pp. 117- 144. Briggs, D.J.; de Hoogh, C.; Gulliver, J.; Wills, J.; Elliott, P.; Kingham, S.; Smallbone, K. 2000. “A Regression based Method for Mapping Traffic-related Air Pollution: Application and Testing in Four Con- trasting Urban Environments.” Sci Total Environ 253(1-3): 151-167. Cicero-Fernandez, P.; Torres, V.; Rosales, A.; Cesar, H.; Dorland, K.; Munoz, R.; Uribe, R.; Martinez, A.P. 2001. “Evaluation of Human Exposure to Ambient PM10 in the Metropolitan area of Mexico City using a GISbased Methodology”. J Air Waste Manag Assoc 51: 1586-1593. Civis, S, Z. Zelinger, M. Strizik, Z. Janour. 2001. “Simulation of Air Pollution in a Wind Tunnel”. In: Spectroscopy from Space. (Demaison, J., Ed.), Kluwer Academic, Dordrecht, 275-299. Cowell PJ and Zeng TQ. 2003. “Integrating Un- certainty Theories with GIS for Modeling Coastal Hazards of Climate Change.” Marine Geodesy, 26, 5-18. Gualtieri G., & Tartaglia M. 1998. Predicting Urban Traffic Air Pollution: a GIS frame- work, Transportation Research - D, 3(5), pp. 329-336, Elsevier Science. Hoek, G.; Fischer, P.; Van Den Brandt, P.; Goldbohm, S.; Brunekreef, B. 2001. “Esti- mation of Long-term Average Exposure to Outdoor Air Pollution for a cohort study on mortality.” J Expo Anal Environ Epidemiol 11(6): 459-469. Holgate. 2002. Air Pollution and Health. Lancet, 360: 1233-1242. Janour, Z., Z. Zelinger, S. Civis. 1999. “Laser Photoacoustic Spectrometry and its Ap- plication for Simulation of Air Pollution in a wind tunnel”. Analyst, 124, 1205-1208. Jones C B, Kidner D B, Luo L Q, Bundy Gl And Ware J M.. 1996. Database design for a Multi-scale Spatial Information System. Int. Journal of GIS, 10(8), pp.901-920. Lave, L.B. and Seskin, E.P., 1972. “Air pollution, climate, and home heating: their effects on US mortality rates”. Am J Public Health 62 7, pp. 909–916 Longley, P.A., M.F. Goodchild, D.J. Maguire, D.W. Rhind. 2001. “Geographic Infor- mation Systems and Science.” John Wiley & Sons, 27- 58, New York. Moaz, Alsherfawi Aljazaerli. 2005. “Hedonic Valuation of Marginal Willingness to Pay for Air Quality in Metropolitan Damas- cus.” Forum of International Development Studies, 3 September 2005. Murty, M., S. Gulati and A. Banerjee. 2003. Hedonic Property Prices and Valuation of Benefits from Reducin Urban Air Pollu-