Research Article
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Year 2020, Volume: 7 Issue: 4, 298 - 307, 31.12.2020

Orcun Moralı Neslihan Yılmaz

https://doi.org/10.17261/Pressacademia.2020.1304

Abstract

References

  • Anselin, L. (1988). Spatial Econometrics: Methods and Models. Springer Netherlands. //www.springer.com/us/book/9789024737352
  • Bárcena, M. J., Menéndez, P., Palacios, M. B., & Tusell, F. (2014). Chapter 10—A Real-Time Property Value Index Based on Web Data. In Y. Zhao & Y. Cen (Eds.), Data Mining Applications with R (pp. 273–297). Academic Press. https://doi.org/10.1016/B978-0-12-411511-8.00010-4
  • Bera, A. K., & Kangalli Uyar, S. G. (2019). Local and global determinants of office rents in Istanbul: The mixed geographically weighted regression approach. Journal of European Real Estate Research, 12(2), 227–249. https://doi.org/10.1108/JERER-12-2018-0052
  • Bitter, C., Mulligan, G. F., & Dall’erba, S. (2007). Incorporating spatial variation in housing attribute prices: A comparison of geographically weighted regression and the spatial expansion method. Journal of Geographical Systems, 9(1), 7–27. https://doi.org/10.1007/s10109-006-0028-7
  • Brunauer, W., Feilmayr, W., & Wagner, K. (2012). A New Residential Property Price Index for Austria. Oesterreichische Nationalbank Statistiken - Daten Und Analysen, Q3-12, 13.
  • Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1996). Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity. Geographical Analysis, 28(4), 281–298. https://doi.org/10.1111/j.1538-4632.1996.tb00936.x
  • Fotheringham, A. S., Charlton, M., & Brunsdon, C. (1997). Two techniques for exploring non-stationarity in geographical data. Geographical Systems, 4(1), 59–82.
  • Fotheringham, A. S., & Oshan, T. M. (2016). Geographically weighted regression and multicollinearity: Dispelling the myth. Journal of Geographical Systems, 18(4), 303–329. https://doi.org/10.1007/s10109-016-0239-5
  • Gollini, I., Lu, B., Charlton, M., Brunsdon, C., & Harris, P. (2015). GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models. Journal of Statistical Software, 63(1), 1–50. https://doi.org/10.18637/jss.v063.i17
  • Hanink, D. M., Cromley, R. G., & Ebenstein, A. Y. (2012). Spatial Variation in the Determinants of House Prices and Apartment Rents in China. The Journal of Real Estate Finance and Economics, 45(2), 347–363. https://doi.org/10.1007/s11146-010-9262-3
  • Huang, B., Wu, B., & Barry, M. (2010). Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices. International Journal of Geographical Information Science, 24(3), 383–401. https://doi.org/10.1080/13658810802672469
  • Hülagü, T., Kızılkaya, E., Özbekler, A. G., & Tunar, P. (2016). A Hedonic House Price Index for Turkey. TCMB Working Paper, No:16/03, 25.
  • Kangallı Uyar, S. G., & Yayla, N. (2016). Konut Fiyatlarının Hedonik Fiyatlama Yaklaşımına Göre Mekansal Ekonometrik Modeller ile Tahmini. Social Sciences (NWSASOS), 11(4), 326–342.
  • Keskin, B., (2008) Hedonic analysis of price in the Istanbul housing market, International Journal of Strategic Property Management, 12(2), 125-138.
  • Koramaz, T. K., & Dokmeci, V. (2012). Spatial Determinants of Housing Price Values in Istanbul. European Planning Studies, 20(7), 1221 1237. https://doi.org/10.1080/09654313.2012.673569
  • Lu, B., Charlton, M., Harris, P., & Fotheringham, A. S. (2014). Geographically weighted regression with a non-Euclidean distance metric: A case study using hedonic house price data. International Journal of Geographical Information Science, 28(4), 660–681. https://doi.org/10.1080/13658816.2013.865739
  • Manganelli, B., Pontrandolfi, P., Azzato, A., & Murgante, B. (2014). Using geographically weighted regression for housing market segmentation. International Journal of Business Intelligence and Data Mining, 9(2), 161. https://doi.org/10.1504/IJBIDM.2014.065100
  • Olszewski, K., Waszczuk, J., & Widłak, M. (2017). Spatial and Hedonic Analysis of House Price Dynamics in Warsaw, Poland. Journal of Urban Planning and Development, 143(3), 04017009. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000394
  • Ozus, E., Dokmeci, V., Kiroglu, G., & Egdemir, G. (2007). Spatial Analysis of Residential Prices in Istanbul. European Planning Studies, 15(5), 707–721. https://doi.org/10.1080/09654310701214085
  • Páez, A., Farber, S., & Wheeler, D. (2011). A Simulation-Based Study of Geographically Weighted Regression as a Method for Investigating Spatially Varying Relationships. Environment and Planning A: Economy and Space, 43(12), 2992–3010. https://doi.org/10.1068/a44111
  • Salvati, L. (2019). Examining urban functions along a metropolitan gradient: A geographically weighted regression tells you more. Letters in Spatial and Resource Sciences, 12(1), 19–40. https://doi.org/10.1007/s12076-018-00221-x
  • Statistical Office of the European Communities, International Labour Organization, International Monetary Fund, Organisation for Economic Co-operation and Development, United Nations, Economic Commission for Europe, & World Bank. (2013). Handbook on residential property prices indices (RPPIs). Publications Office of the European Union. https://doi.org/10.1787/9789264197183-en
  • Wheeler, D., & Tiefelsdorf, M. (2005). Multicollinearity and correlation among local regression coefficients in geographically weighted regression. Journal of Geographical Systems, 7(2), 161–187. https://doi.org/10.1007/s10109-005-0155-6
  • Wu, B., Li, R., & Huang, B. (2014). A geographically and temporally weighted autoregressive model with application to housing prices. International Journal of Geographical Information Science, 28(5), 1186–1204. https://doi.org/10.1080/13658816.2013.878463
  • Yao, J., & Fotheringham, A. S. (2016). Local Spatiotemporal Modeling of House Prices: A Mixed Model Approach. The Professional Geographer, 68(2), 189–201. https://doi.org/10.1080/00330124.2015.1033671

SPATIAL HETEROGENEITY IN ISTANBUL HOUSING MARKET: A GEOGRAPHICALLY WEIGHTED APPROACH

Year 2020, Volume: 7 Issue: 4, 298 - 307, 31.12.2020

Orcun Moralı Neslihan Yılmaz

https://doi.org/10.17261/Pressacademia.2020.1304

Abstract

Purpose - This study examines and documents spatial heterogeneity in Istanbul housing market using Geographically Weighted Model (GWR).
Methodology - A GWR model with a Gaussian kernel and an adaptive bandwidth based on cross-validation is employed on a cross-sectional housing listing data set. Additional analysis is provided using geographically weighted Spearman’s rank correlation measure between prices and variables.
Findings- GWR model substantially boosts goodness of fit in our pricing model compared to a standard hedonic regression model. The variation within GWR coefficients is high and of micro nature. Median GWR coefficients often differ from standard hedonic regression coefficients. The variability of coefficients is plotted on map.
Conclusion- Findings suggest the existence of spatial non-stationarity in standard hedonic regressions and favor the use of models appropriate for spatial heterogeneity. Findings encourage further research in hedonic models applications such as in quality adjustments to price indices.

Keywords

Spatial heterogeneity spatial non-stationarity geographically weighted regression Istanbul housing market quality adjusted price index

References

  • Anselin, L. (1988). Spatial Econometrics: Methods and Models. Springer Netherlands. //www.springer.com/us/book/9789024737352
  • Bárcena, M. J., Menéndez, P., Palacios, M. B., & Tusell, F. (2014). Chapter 10—A Real-Time Property Value Index Based on Web Data. In Y. Zhao & Y. Cen (Eds.), Data Mining Applications with R (pp. 273–297). Academic Press. https://doi.org/10.1016/B978-0-12-411511-8.00010-4
  • Bera, A. K., & Kangalli Uyar, S. G. (2019). Local and global determinants of office rents in Istanbul: The mixed geographically weighted regression approach. Journal of European Real Estate Research, 12(2), 227–249. https://doi.org/10.1108/JERER-12-2018-0052
  • Bitter, C., Mulligan, G. F., & Dall’erba, S. (2007). Incorporating spatial variation in housing attribute prices: A comparison of geographically weighted regression and the spatial expansion method. Journal of Geographical Systems, 9(1), 7–27. https://doi.org/10.1007/s10109-006-0028-7
  • Brunauer, W., Feilmayr, W., & Wagner, K. (2012). A New Residential Property Price Index for Austria. Oesterreichische Nationalbank Statistiken - Daten Und Analysen, Q3-12, 13.
  • Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1996). Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity. Geographical Analysis, 28(4), 281–298. https://doi.org/10.1111/j.1538-4632.1996.tb00936.x
  • Fotheringham, A. S., Charlton, M., & Brunsdon, C. (1997). Two techniques for exploring non-stationarity in geographical data. Geographical Systems, 4(1), 59–82.
  • Fotheringham, A. S., & Oshan, T. M. (2016). Geographically weighted regression and multicollinearity: Dispelling the myth. Journal of Geographical Systems, 18(4), 303–329. https://doi.org/10.1007/s10109-016-0239-5
  • Gollini, I., Lu, B., Charlton, M., Brunsdon, C., & Harris, P. (2015). GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models. Journal of Statistical Software, 63(1), 1–50. https://doi.org/10.18637/jss.v063.i17
  • Hanink, D. M., Cromley, R. G., & Ebenstein, A. Y. (2012). Spatial Variation in the Determinants of House Prices and Apartment Rents in China. The Journal of Real Estate Finance and Economics, 45(2), 347–363. https://doi.org/10.1007/s11146-010-9262-3
  • Huang, B., Wu, B., & Barry, M. (2010). Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices. International Journal of Geographical Information Science, 24(3), 383–401. https://doi.org/10.1080/13658810802672469
  • Hülagü, T., Kızılkaya, E., Özbekler, A. G., & Tunar, P. (2016). A Hedonic House Price Index for Turkey. TCMB Working Paper, No:16/03, 25.
  • Kangallı Uyar, S. G., & Yayla, N. (2016). Konut Fiyatlarının Hedonik Fiyatlama Yaklaşımına Göre Mekansal Ekonometrik Modeller ile Tahmini. Social Sciences (NWSASOS), 11(4), 326–342.
  • Keskin, B., (2008) Hedonic analysis of price in the Istanbul housing market, International Journal of Strategic Property Management, 12(2), 125-138.
  • Koramaz, T. K., & Dokmeci, V. (2012). Spatial Determinants of Housing Price Values in Istanbul. European Planning Studies, 20(7), 1221 1237. https://doi.org/10.1080/09654313.2012.673569
  • Lu, B., Charlton, M., Harris, P., & Fotheringham, A. S. (2014). Geographically weighted regression with a non-Euclidean distance metric: A case study using hedonic house price data. International Journal of Geographical Information Science, 28(4), 660–681. https://doi.org/10.1080/13658816.2013.865739
  • Manganelli, B., Pontrandolfi, P., Azzato, A., & Murgante, B. (2014). Using geographically weighted regression for housing market segmentation. International Journal of Business Intelligence and Data Mining, 9(2), 161. https://doi.org/10.1504/IJBIDM.2014.065100
  • Olszewski, K., Waszczuk, J., & Widłak, M. (2017). Spatial and Hedonic Analysis of House Price Dynamics in Warsaw, Poland. Journal of Urban Planning and Development, 143(3), 04017009. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000394
  • Ozus, E., Dokmeci, V., Kiroglu, G., & Egdemir, G. (2007). Spatial Analysis of Residential Prices in Istanbul. European Planning Studies, 15(5), 707–721. https://doi.org/10.1080/09654310701214085
  • Páez, A., Farber, S., & Wheeler, D. (2011). A Simulation-Based Study of Geographically Weighted Regression as a Method for Investigating Spatially Varying Relationships. Environment and Planning A: Economy and Space, 43(12), 2992–3010. https://doi.org/10.1068/a44111
  • Salvati, L. (2019). Examining urban functions along a metropolitan gradient: A geographically weighted regression tells you more. Letters in Spatial and Resource Sciences, 12(1), 19–40. https://doi.org/10.1007/s12076-018-00221-x
  • Statistical Office of the European Communities, International Labour Organization, International Monetary Fund, Organisation for Economic Co-operation and Development, United Nations, Economic Commission for Europe, & World Bank. (2013). Handbook on residential property prices indices (RPPIs). Publications Office of the European Union. https://doi.org/10.1787/9789264197183-en
  • Wheeler, D., & Tiefelsdorf, M. (2005). Multicollinearity and correlation among local regression coefficients in geographically weighted regression. Journal of Geographical Systems, 7(2), 161–187. https://doi.org/10.1007/s10109-005-0155-6
  • Wu, B., Li, R., & Huang, B. (2014). A geographically and temporally weighted autoregressive model with application to housing prices. International Journal of Geographical Information Science, 28(5), 1186–1204. https://doi.org/10.1080/13658816.2013.878463
  • Yao, J., & Fotheringham, A. S. (2016). Local Spatiotemporal Modeling of House Prices: A Mixed Model Approach. The Professional Geographer, 68(2), 189–201. https://doi.org/10.1080/00330124.2015.1033671
There are 25 citations in total.

Details

Primary Language English
Subjects Economics, Finance, Business Administration
Journal Section Articles
Authors

Orcun Moralı This is me 0000-0002-1861-302X

Neslihan Yılmaz This is me 0000-0002-5648-7343

Publication Date December 31, 2020
Published in Issue Year 2020 Volume: 7 Issue: 4

Cite

APA Moralı, O., & Yılmaz, N. (2020). SPATIAL HETEROGENEITY IN ISTANBUL HOUSING MARKET: A GEOGRAPHICALLY WEIGHTED APPROACH. Journal of Economics Finance and Accounting, 7(4), 298-307. https://doi.org/10.17261/Pressacademia.2020.1304

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