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TÜRKİYE’DEKİ KONUTLARIN KİRA DEĞERİNİN ANALİZİ: HEDONİK MODEL VE YAPAY SİNİR AĞLARI YAKLAŞIMI

Year 2009, Volume: 1 Issue: 1, 73 - 90, 31.01.2009

Sibel Selim Ayça Demirbilek

Abstract

Emlak değerlemede ve konut piyasası araştırmalarında konutun piyasa değeri ve kira değeri genellikle mikro ekonomik teoriye dayanan hedonik model yoluyla analiz edilmektedir. Hedonik model, bir malın özelliklerinin fiyat üzerindeki etkisini incelemektedir. Bu çalışmada Türkiye’de konut kira değerlerini belirleyen faktörler 2004 Hanehalkı Bütçe Anketi kullanılarak analiz edilmiştir. Ele alınan modelin doğrusal olmama özelliğinden dolayı yapay sinir ağları (YSA) alternatif bir yaklaşım olarak kullanılmıştır. Çalışmada hedonik regresyon modeli ile yapay sinir ağları modelinin tahmin performansı karşılaştırılmış ve konutların kira değerlerinin tahminlenmesinde yapay sinir ağlarının daha iyi alternatif bir yöntem olduğu belirlenmiştir.

Keywords

Konut kira değeri Hedonik model Yapay sinir ağları Türkiye

References

  • ADAIR, A.; McGREAL, S.;SMYTH, A.; COOPER, J. ve RYLEY, T. (2000)
  • House price and accessibility: The testing of relationships within the Belfast urban area, Housing Studies, 15(5), (s: 699-716). BAO, H. X. H. ve WAN, A. T. K. (2004) On the use of spline smoothing in estimating hedonic housing price models: Emprical evidence using Hong
  • Kong data, Real Estate Economics, 32(3), (s: 487-507). BIN, O. (2004). A prediction comparison of housing sales prices by parametric versus semi-parametric regressions, Journal of Housing Economics. 13, (s: 68-84).
  • BORST, R.A. (1991). Artificial Neural Networks: The Next Modelling/Calibration
  • Technology for the Assessment Community? Property Tax Journal, IAAO, 10(1), (s: 69-94). BOX, G. ve COX, D. (1964) An analysis of transformations, Journal of the Royal
  • Statistical Society, B 26, (s: 211–252). BRASINGTON, D. M.ve HITE, D. (2005) Demand for environmental quality: a spatial hedonic analysis, Regional Science and Urban Economics, 35, (s: 82).
  • CROPPER, M.;DECK, L. ve McCONNELL, K. (1988) On the choice of functional form for hedonic price functions, Review of Economics and Statistics, , (s: 668–675).
  • CURRY B.;MORGAN P. ve SILVER M. (2002) Neural networks and non-linear statistical methods: An application to the modelling of price-quality relationships, Computers & Operations Research, 29, (s: 951-969).
  • DIN A.;Hoesli M.ve BENDER A. (2001) Environmental variables and real estate prices, Urban Studies, 38(11), (s: 1989-2000).
  • EVANS, A.; JAMES, H. ve COLLİNS, A. (1993) Artificial neural networks: An application to residential valuation in the UK, Journal of Property
  • Valuation & Investment, 11, (s:195-204). FAN, G.; ONG, Z. S. E. ve KOH, H. C. (2006) Determinants of house price: a decision tree approach, Urban Studies, 43(12), 2301-2315.
  • FILHO, C. M. ve BİN, O. (2005) Estimation of hedonic price functions via additive nonparametric regression, Emprical Economics, 30, 93-114.
  • FLEMING, M.C. ve NELLIS, J.G. (1984) The Halifax House Price Index:
  • Technical Details, Halifax Building Society, Halifax. FLETCHER, M.; GALLIMORE, P. ve MANGAN, J. (2000) Heteroscedasticity in hedonic house price models, Journal of Property Research, 17(2), 93
  • HALVORSEN, R. ve PALMQUİST R. (1980) The interpretation of dummy variables in semilogrithmic regressions, American Economic Review, 70, June, 474-5.
  • HALVORSEN, R. ve POLLAKOWSKI, H. (1981) Choice of functional form for hedonic price equations, Journal of Urban Economics, 10, 37–49.
  • HIDANO, N. (2002) The Economic Valuation of the Environment and Public
  • Policy: A Hedonic Approach, New Horizons in Environmental Economics, Series Editors, Wallace E. Oates ve Henk Folmer. HUH S. ve KWAK S.J. (1997) The choice of functional form and variables in the hedonic price model in Seoul, Urban Studies, 34(7). (s:989-998).
  • JANSSEN, C. B. ve SODERBERG, J. Z. (2001) Robust estimation of hedonic models of prive and income for investment property, Journal of Property
  • Investment & Finance, 19(4), (s:342-360). JIM, C. Y. ve CHEN, W. Y. (2007). Consumption preferences and environmental externalities: A hedonic analysis of the housing market in Guangzhou, Geoforum, 38, (s: 414-431).
  • KAUKO T.; HOOİMEİJER P. ve HAKFOORT J. (2002). Capturing housing market segmentation: An alternative approach based on neural network modelling, Housing Studies, 17(6), (s: 875-894).
  • KAUKO T. (2003). On current neural network applications involving spatial modelling of property prices, Journal of Housing and the Built Environment, 18, (s: 159-181).
  • KESBİÇ, Y.;BALDEMİR, E. ve İNCİ, M. (2007) Emlak piyasasında hedonik talep parametrelerinin Tahminlenmesi: Muğla Örneği. 8. Türkiye Ekonomeri ve İstatistik Kongresi, 24-25 Mayıs, İnönü Üniversitesi, Malatya.
  • KESTENS, Y., THERIAULt, M. ve ROSIER, F.D. (2006). Heterogeneity in hedonic modelling of house prices: Looking at buyers’ household profiles, J. Geograph Syst., 8, (s: 61-96).
  • KIM, K. ve PARK, J. (2005). Segmentation of the housing market and its determinants: Seoul and its neighbouring new towns in Korea,
  • Australian Geographer, 36(2), (s: 221-232). LANCASTER, K. J. (1966). A new approach to consumer theory, Journal of
  • Political Economy, 74, (s: 132–157). LIU, J., G.; ZHANG X., L. ve WU W., P. (2006) Application of fuzzy neural network for real estate prediction, LNCS, 3973:1187-1191.
  • MALPEZZI, S. (2003). Hedonic pricing models: A selective and applied Review, in: T.O’Sullivan and K. Gibb (Eds) Housing Economics and Public
  • Policy, 67–89, Malden, MA: Blackwell Science. MCCLUSKEY, W., Dyson, K., Mcfall, D. ve Anand,S. (1996). Mass Appraisal for
  • Property Taxation: An Artificial Intelligence Approach, Land Economics Review, 2(1), (s: 25-32). MURRAY, J. ve SARANTIS, N. (1999). Price-quality relations and hedonic price indexes for cars in the United Kingdom, International Journal of the Economics of Business, 6(1), February.
  • ÖZKAN, G. ve YALPIR, Ş. (2005). Taşınmaza ekonomik bakış ve değerlendirmesi, TMMOB Harita ve Kadastro Mühendisleri Odası 10.
  • Türkiye Harita Bilimsel ve Teknik Kurultayı. 28 Mart-1 Nisan, Ankara. ÖZUS, E. ve DÖKMECİ, V. (2006). Dönüşüm yaşanan tarihi alanlarda konut fiyatlarında etkili faktörlerin analizi, İTÜ Dergisi/a Mimarlık, Planlama, Tasarım, 5(2), (s: 177-186).
  • PAGOURTZI, E.; ASSİMAKOPOULOS, V.; HATZİCHRİSTOS, T. ve French, N. (2003). Real estate appraisal: A review of valuation methods, Journal of Property Investment & Finance, 21(4), 383-401.
  • ROSEN, S. (1974). Hedonic prices and implicit markets: Product differentiation in pure competition, Journal of Political Economy, 82, January/February.
  • ROSSINI P.;MARANO W., Kupke V. ve BURNS M. (2002). A comparison of models measuring the implicit price effect of aircraft noise, 8th Pacific
  • Rim Real Estate Society Conference Christchurch, January, New Zealand. STEVENSON, S. (2004). New emprical evidence on heteroscedasticity in hedonic housing models, Journal of Housing Economics, 13, (s:136-153).
  • SCHULZ, R. ve WERWATZ, A. (2004). A state space model for berlin house prices: estimation and economic interpretation, Journal of Real Estate
  • Finance and Economics, 28, 37-57. TEMURLENK, M. S. ve ÖZÇELIK, A. (2003). Erzurum’da konut kiralarının hedonic model yaklaşımıyla incelenmesi, VI. Ulusal Ekonometri ve İstatistik Sempozyumu, Gazi Üniversitesi, Ankara.
  • TÜİK-Türkiye İstatistik Kurumu (2004) Hanehalkı Bütçe Anketi Araştırması.
  • USTAOĞLU, E. (2003). Hedonic Price Analysis of Office Rents: A Case Study of the Office Market in Ankara, Orta Doğu Teknik Üniversitesi,
  • Yayınlanmamış Yüksek Lisans Tezi. WHİTE, H. (1980). Heteroskedasticity-consistent covariance matrix and a direct test for heteroskedasticity, Econometrica, 48, 817-838.
  • WORZALA, E., LENK, M. ve SİLVa, A. (1995). An exploration of neural networks and its application to real estate valuation, The Journal of Real Estate Research, 10(2).
  • YURTOĞLU, H. (2005), Yapay Sinir Ağları Metodolojisi ile Öngörü Modellemesi:
  • Bazı Makroekonomik Değişkenler için Türkiye Örneği, Devlet Planlama Teşkilatı, Ekonomik Modeller ve Stratejik Araştırmalar Genel Müdürlüğü, Uzmanlık Tezi, No: 2683.
  • Tablo 5: Hedonik Model ve YSA’nın Performanslarının Karşılaştırılması Performans Ölçüleri Ortalama kare hata (MSE) Ortalama kare hatanın karekökü (RMSE) Ortalama mutlak hata (MAE) Hedonik Model 6952 8338 7448 3478

Year 2009, Volume: 1 Issue: 1, 73 - 90, 31.01.2009

Sibel Selim Ayça Demirbilek

Abstract

In real estate valuation and house market research, house prices and rental value are generally analyzed by hedonic model based on micro economic theory. Hedonic model examines the effect of characteristics of goods on their prices. Factors that determine the rental value of houses in Turkey are analyzed in this paper using 2004 Household Budget Survey Data. Because of potential non-linearity in the hedonic functions, artificial neural network (ANN) is employed in this study as an alternative method. By comparing the prediction performance between the hedonic regression and ANN models, this study demonstrates that ANN is a better alternative for prediction of the house rental prices in Turkey.

Keywords

Rental value of house Hedonic model Artificial neural network Turkey

References

  • ADAIR, A.; McGREAL, S.;SMYTH, A.; COOPER, J. ve RYLEY, T. (2000)
  • House price and accessibility: The testing of relationships within the Belfast urban area, Housing Studies, 15(5), (s: 699-716). BAO, H. X. H. ve WAN, A. T. K. (2004) On the use of spline smoothing in estimating hedonic housing price models: Emprical evidence using Hong
  • Kong data, Real Estate Economics, 32(3), (s: 487-507). BIN, O. (2004). A prediction comparison of housing sales prices by parametric versus semi-parametric regressions, Journal of Housing Economics. 13, (s: 68-84).
  • BORST, R.A. (1991). Artificial Neural Networks: The Next Modelling/Calibration
  • Technology for the Assessment Community? Property Tax Journal, IAAO, 10(1), (s: 69-94). BOX, G. ve COX, D. (1964) An analysis of transformations, Journal of the Royal
  • Statistical Society, B 26, (s: 211–252). BRASINGTON, D. M.ve HITE, D. (2005) Demand for environmental quality: a spatial hedonic analysis, Regional Science and Urban Economics, 35, (s: 82).
  • CROPPER, M.;DECK, L. ve McCONNELL, K. (1988) On the choice of functional form for hedonic price functions, Review of Economics and Statistics, , (s: 668–675).
  • CURRY B.;MORGAN P. ve SILVER M. (2002) Neural networks and non-linear statistical methods: An application to the modelling of price-quality relationships, Computers & Operations Research, 29, (s: 951-969).
  • DIN A.;Hoesli M.ve BENDER A. (2001) Environmental variables and real estate prices, Urban Studies, 38(11), (s: 1989-2000).
  • EVANS, A.; JAMES, H. ve COLLİNS, A. (1993) Artificial neural networks: An application to residential valuation in the UK, Journal of Property
  • Valuation & Investment, 11, (s:195-204). FAN, G.; ONG, Z. S. E. ve KOH, H. C. (2006) Determinants of house price: a decision tree approach, Urban Studies, 43(12), 2301-2315.
  • FILHO, C. M. ve BİN, O. (2005) Estimation of hedonic price functions via additive nonparametric regression, Emprical Economics, 30, 93-114.
  • FLEMING, M.C. ve NELLIS, J.G. (1984) The Halifax House Price Index:
  • Technical Details, Halifax Building Society, Halifax. FLETCHER, M.; GALLIMORE, P. ve MANGAN, J. (2000) Heteroscedasticity in hedonic house price models, Journal of Property Research, 17(2), 93
  • HALVORSEN, R. ve PALMQUİST R. (1980) The interpretation of dummy variables in semilogrithmic regressions, American Economic Review, 70, June, 474-5.
  • HALVORSEN, R. ve POLLAKOWSKI, H. (1981) Choice of functional form for hedonic price equations, Journal of Urban Economics, 10, 37–49.
  • HIDANO, N. (2002) The Economic Valuation of the Environment and Public
  • Policy: A Hedonic Approach, New Horizons in Environmental Economics, Series Editors, Wallace E. Oates ve Henk Folmer. HUH S. ve KWAK S.J. (1997) The choice of functional form and variables in the hedonic price model in Seoul, Urban Studies, 34(7). (s:989-998).
  • JANSSEN, C. B. ve SODERBERG, J. Z. (2001) Robust estimation of hedonic models of prive and income for investment property, Journal of Property
  • Investment & Finance, 19(4), (s:342-360). JIM, C. Y. ve CHEN, W. Y. (2007). Consumption preferences and environmental externalities: A hedonic analysis of the housing market in Guangzhou, Geoforum, 38, (s: 414-431).
  • KAUKO T.; HOOİMEİJER P. ve HAKFOORT J. (2002). Capturing housing market segmentation: An alternative approach based on neural network modelling, Housing Studies, 17(6), (s: 875-894).
  • KAUKO T. (2003). On current neural network applications involving spatial modelling of property prices, Journal of Housing and the Built Environment, 18, (s: 159-181).
  • KESBİÇ, Y.;BALDEMİR, E. ve İNCİ, M. (2007) Emlak piyasasında hedonik talep parametrelerinin Tahminlenmesi: Muğla Örneği. 8. Türkiye Ekonomeri ve İstatistik Kongresi, 24-25 Mayıs, İnönü Üniversitesi, Malatya.
  • KESTENS, Y., THERIAULt, M. ve ROSIER, F.D. (2006). Heterogeneity in hedonic modelling of house prices: Looking at buyers’ household profiles, J. Geograph Syst., 8, (s: 61-96).
  • KIM, K. ve PARK, J. (2005). Segmentation of the housing market and its determinants: Seoul and its neighbouring new towns in Korea,
  • Australian Geographer, 36(2), (s: 221-232). LANCASTER, K. J. (1966). A new approach to consumer theory, Journal of
  • Political Economy, 74, (s: 132–157). LIU, J., G.; ZHANG X., L. ve WU W., P. (2006) Application of fuzzy neural network for real estate prediction, LNCS, 3973:1187-1191.
  • MALPEZZI, S. (2003). Hedonic pricing models: A selective and applied Review, in: T.O’Sullivan and K. Gibb (Eds) Housing Economics and Public
  • Policy, 67–89, Malden, MA: Blackwell Science. MCCLUSKEY, W., Dyson, K., Mcfall, D. ve Anand,S. (1996). Mass Appraisal for
  • Property Taxation: An Artificial Intelligence Approach, Land Economics Review, 2(1), (s: 25-32). MURRAY, J. ve SARANTIS, N. (1999). Price-quality relations and hedonic price indexes for cars in the United Kingdom, International Journal of the Economics of Business, 6(1), February.
  • ÖZKAN, G. ve YALPIR, Ş. (2005). Taşınmaza ekonomik bakış ve değerlendirmesi, TMMOB Harita ve Kadastro Mühendisleri Odası 10.
  • Türkiye Harita Bilimsel ve Teknik Kurultayı. 28 Mart-1 Nisan, Ankara. ÖZUS, E. ve DÖKMECİ, V. (2006). Dönüşüm yaşanan tarihi alanlarda konut fiyatlarında etkili faktörlerin analizi, İTÜ Dergisi/a Mimarlık, Planlama, Tasarım, 5(2), (s: 177-186).
  • PAGOURTZI, E.; ASSİMAKOPOULOS, V.; HATZİCHRİSTOS, T. ve French, N. (2003). Real estate appraisal: A review of valuation methods, Journal of Property Investment & Finance, 21(4), 383-401.
  • ROSEN, S. (1974). Hedonic prices and implicit markets: Product differentiation in pure competition, Journal of Political Economy, 82, January/February.
  • ROSSINI P.;MARANO W., Kupke V. ve BURNS M. (2002). A comparison of models measuring the implicit price effect of aircraft noise, 8th Pacific
  • Rim Real Estate Society Conference Christchurch, January, New Zealand. STEVENSON, S. (2004). New emprical evidence on heteroscedasticity in hedonic housing models, Journal of Housing Economics, 13, (s:136-153).
  • SCHULZ, R. ve WERWATZ, A. (2004). A state space model for berlin house prices: estimation and economic interpretation, Journal of Real Estate
  • Finance and Economics, 28, 37-57. TEMURLENK, M. S. ve ÖZÇELIK, A. (2003). Erzurum’da konut kiralarının hedonic model yaklaşımıyla incelenmesi, VI. Ulusal Ekonometri ve İstatistik Sempozyumu, Gazi Üniversitesi, Ankara.
  • TÜİK-Türkiye İstatistik Kurumu (2004) Hanehalkı Bütçe Anketi Araştırması.
  • USTAOĞLU, E. (2003). Hedonic Price Analysis of Office Rents: A Case Study of the Office Market in Ankara, Orta Doğu Teknik Üniversitesi,
  • Yayınlanmamış Yüksek Lisans Tezi. WHİTE, H. (1980). Heteroskedasticity-consistent covariance matrix and a direct test for heteroskedasticity, Econometrica, 48, 817-838.
  • WORZALA, E., LENK, M. ve SİLVa, A. (1995). An exploration of neural networks and its application to real estate valuation, The Journal of Real Estate Research, 10(2).
  • YURTOĞLU, H. (2005), Yapay Sinir Ağları Metodolojisi ile Öngörü Modellemesi:
  • Bazı Makroekonomik Değişkenler için Türkiye Örneği, Devlet Planlama Teşkilatı, Ekonomik Modeller ve Stratejik Araştırmalar Genel Müdürlüğü, Uzmanlık Tezi, No: 2683.
  • Tablo 5: Hedonik Model ve YSA’nın Performanslarının Karşılaştırılması Performans Ölçüleri Ortalama kare hata (MSE) Ortalama kare hatanın karekökü (RMSE) Ortalama mutlak hata (MAE) Hedonik Model 6952 8338 7448 3478
There are 45 citations in total.

Details

Other ID JA44CP98CB
Journal Section Review Article
Authors

Sibel Selim

Ayça Demirbilek

Publication Date January 31, 2009
Published in Issue Year 2009Volume: 1 Issue: 1

Cite

APA Selim, S., & Demirbilek, A. (2009). TÜRKİYE’DEKİ KONUTLARIN KİRA DEĞERİNİN ANALİZİ: HEDONİK MODEL VE YAPAY SİNİR AĞLARI YAKLAŞIMI. Aksaray Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, 1(1), 73-90.
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