The Impact of Macroeconomic Factors on Residential Property Price Indices in Europe

• Authors: Małgorzata Renigier-Biłozor , Radosław Wiśniewski
• Languages of publication: EN

Abstracts

EN

This paper aims to determine the influence of selected variables on residential property price indices for the European countries, with particular attention paid to Italy and Poland, using a rough set theory and an approach that uses a committee of artificial neural networks. Additionally, the overall analysis for each European country is presented. Quarterly time series data constituted the material for testing and empirical results. The developed models show that the economic and financial situation of European countries affects residential property markets. Residential property markets are connected, despite the fact that they are situated in different parts of Europe. The economic and financial crisis of countries has variable influence on prices of real estate. The results also suggest that methodology based on the rough set theory and a committee of artificial neural networks has the ability to learn, generalize, and converge the residential property prices index.

Keywords

EN

residential property prices indices; impact of macroeconomic factors; neural networks; rough set theory

• Publisher: Sciendo
• Journal: Folia Oeconomica Stetinensia
• Year: 2012
• Volume: 12
• Issue: 2
• Pages: 103-125

Dates

published

2012-12-01

online

2013-07-30

Contributors

author

Małgorzata Renigier-Biłozor

• University of Warmia and Mazury in Olsztyn, Poland Faculty of Geodesy and Land Management Department of Real Estate Management and Regional Development Prawochenskiego 15, 10-724 Olsztyn, Poland

author

Radosław Wiśniewski

• University of Warmia and Mazury in Olsztyn, Poland Faculty of Geodesy and Land Management Department of Real Estate Management and Regional Development Prawochenskiego 15, 10-724 Olsztyn, Poland

References

• Agnello, L. & Schuknecht, L. (2011). Booms and busts in housing markets: Determinants and implications. J. Housing Econ., DOI: 10.1016/j.jhe.2011.04.001.[Crossref]
• Bao, H.X.H. & Wan, A.T.K. (2004). On the use of spline smoothing in estimating hedonic housing price models: Empirical evidence using Hong Kong data. Real Estate Economics, 32 (3).[Crossref]
• Bełej, M. & Kulesza, S. (2012). Modeling the real estate prices in Olsztyn under instability conditions. Folia Oeconomica Stetinensia, Vol. 11 (19).
• Bishop, C. (1995). Neural Networks for Pattern Recognition. Oxford: University Press.
• Borst, R.A. & McCluskey, W. (1996). The Role of Artificial Neural Networks in the Mass Appraisalof Real Estate. Paper presented to the Third European Real Estate Society Conference, Belfast, June 26-28.
• Borst, R.A. (1991). Artificial Neural Networks: The Next Modelling/Calibration Technology for the Assessment Community? Property Tax Journal, IAAO, 10 (1), 69-94.
• Borst, R.A. (1995). Artificial neural networks in mass appraisal. Journal of Property Tax Assessment&Administration, 1 (2), 5-15
• Calhoun, C.A. (2003). Property Valuation Models and House Price Indexes for The Provinces of Thailand: 1992-2000. Housing Finance International, 17.
• Curry, B., Morgan, P. & Silver, M. (2002). Neural networks and nonlinear statistical methods: An application to the modelling of pricequality relationships. Computers & OperationsResearch, 29.
• d’Amato, M. (2007). Comparing Rough Set Theory with Multiple Regression Analysis as Automated Valuation Methodologies. International Real Estate Review, Vol. 2.
• d’Amato, M. (2008). Rough Set Theory as Property Valuation Methodology: The whole story. In: T. Kauko, M. d’Amato (Eds.), Mass Appraisal Methods. An international perspectivefor property valuers. Oxford: Blackwell Publishing. RICS Research.
• Deja, R. (2000). Zastosowanie teorii zbiorów przybliżonych w analizie konfliktów. PhD thesis. Instytut Podstaw Informatyki Polskiej Akademii Nauk.
• Do, A.Q. & Grudnitiski, G. (1992). A Neural Network Approach to Residential Property Appraisal. The Real Estate Appraiser.
• Evans, A., James, H. & Collins, A. (1993). Artificial Neural Networks: an Application to Residential Valuation in the UK. Journal of Property Valuation & Investment, 11.
• Fan, G., Ong, Z.S.E. & Koh, H.C. (2006). Determinants of house price: A decision tree approach. Urban Studies, 43 (12).[Crossref]
• Filho, C.M. & Bin, O. (2005). Estimation of hedonic price functions via additive nonparametric regression. Empirical Economics, 30 (1).
• Fletcher, M., Mangan, J. & Raeburn, E. (2004). Comparing hedonic models for estimating and forecasting house prices. Property Management, 22 (3).
• Frew J., & Jud, G.D. (2003). Estimating The Value of Apartment Buildings. The J. Real EstateRes., 25.
• Geman, S., Bienenstock, E. & Doursat, R. (1992). Neural Networks and the bias-variance dilemma. Neural Computation 4.
• Goodhart, C. (2005). Beyond current policy frameworks. Working Papers 189. BIS.
• Jacobs, R.A. (1997). Bias/Variance Analyses of Mixtures-of-Experts Architectures. NeuralComputation, 9 (2).[PubMed]
• Janssen, C., Söderberg, B. & Zhou, J. (2001). Robust estimation of hedonic models of price and income for investment property. Journal of Property Investment & Finance, 19 (4).
• Kim, K. & Park, J. (2005). Segmentation of the housing market and its determinants: Seoul and its neighbouring new towns in Korea. Australian Geographer, 36 (2).[Crossref]
• Komorowski, J., Pawlak, Z., Polkowski, L. & Skowron, A. (1999). Rough sets: A tutorial. In: S.K. Pal, A. Skowron (Eds.), Rough fuzzy hybridization: A new trend in decision making, Singapore: Springer-Verlag.
• Kontrimasa, V. & Verikasb, A. (2011). The mass appraisal of the real estate by computational intelligence. Applied Soft Computing, 11.[Crossref][WoS]
• Kotkowski, B. & Ratajczak, W. (2002). Zbiory przybliżone w analizie danych geograficznych. In: H. Rogacki (Ed.), Możliwości i ograniczenia zastosowań metod badawczych w geografiispołeczno-ekonomicznej i gospodarce przestrzennej. Poznań: Bogucki Wydawnictwo Naukowe.
• Limsombunchai, V., Gan, Ch. & Lee, M. (2004). House Price Prediction: Hedonic Price Model vs. Artificial Neural Network. American Journal of Applied Sciences 1 (3).
• Liu, J.G., Zhang, X.L. & Wu W.P. (2006). Application of Fuzzy Neural Network for Real Estate Prediction. Lecture Notes in Computer Science. NUMB 3973.
• McCluskey, W., Dyson, K., McFall, D. & Anand S. (1996). Mass Appraisal for Property Taxation: An Artificial Intelligence Approach. Land Economics Review, Vol. 2, No. 1.
• Mrózek, A. & Płonka, L. (1999). Analiza danych metoda zbiorów przybliżonych. Warszawa: Akademicka Oficyna Wydawnicza PLJ.
• Nguyen, N. & Cripps, A. (2001). Predicting housing value: A comparison of multiple regression analysis and artificial neural network. Journal of Real Estate Research, 3 (22).
• Pawlak, Z. (1982). Rough sets. International Journal of Information and Computer Science, 11.
• Pawlak, Z. (1991). Rough sets: Theoretical aspects of reasoning abaut data. Kluwer Academic Press.
• Pawlak, Z. (1997). Rough Sets and their Applications. Seminar Department of Computing - Macquarie University.
• Renigier-Biłozor, M. & Biłozor, A. (2007). Application of the rough set theory and the fuzzyset theory in land management. Londyn: Annual conference The European Real Estate Society - ERES.
• Renigier-Biłozor, M. & Biłozor, A. (2009). The significance of real estate attributes in theprocess of determining land function with the use of the rough set theory. Olsztyn: Wyd. Studia i Materiały Towarzystwa Naukowego Nieruchomości.
• Renigier-Biłozor, M. (2008). Zastosowanie teorii zbiorów przybliżonych do masowej wycenynieruchomości na małych rynkach (Application of rough sets theory for mass valuation of real estate in small markets). ACTA SCIENTIARUM POLONORUM, Administratio Locorum 7 (3).
• Renigier-Biłozor, M. (2011). Analysis of Real Estate Markets With the Use of the Rough set Theory. Journal of the Polish Real Estate Scientific Society, Vol. 19, No. 3.
• Rossini, P.A. (1997). Application of Artificial Neural Networks to the Valuation of Residential Property 3rd Pacific Rim Real Estate Society Conference, New Zealand. Rough Sets in Knowledge Discovery 2: Applications, Case Studies and Software Systems (1998). L. Polkowski, A. Skowron (Eds.). Physica-Verlag, Heidelberg.
• Selim, H. (2009). Determinants of House Prices in Turkey: Hedonic regression versus artificial neural network. Expert Systems with Applications, 36.
• Słowiński, R. (1992). Intelligent Decision Support. Handbook of Applications and Advances ofthe Rough Sets Theory. Dordrecht: Kluwer Academic Publishers.
• Stefanowski, J. & Tsoukias, A. (2000). Valued Tolerance and Deciosion Rules. In: W. Ziarko, Y. Yao (Eds.). Proceedings of the RSCTC 2000 Conference, Banff.
• Stefanowski, J. (2001). Algorytmy indukcji reguł decyzyjnych w odkrywaniu wiedzy. Habilitation thesis. Poznań: Wydawnictwo Politechniki Poznańskiej.
• Stevenson, S. (2004). New Empirical Evidence on Heteroscedasticity in Hedonic Housing Models. Journal of Housing Economics, 13.
• Tay, D. & Ho, D. (1992). Artificial Intelligence and the Mass Appraisal of Residential Apartment. Journal of Property Valuation & Investment, 10. Tay, D. & Ho, D. (1994). Intelligent Mass Appraisal. Journal of Property Tax Assessment & Administration, Vol. 1, No. 1.
• Wiśniewski, R. (2003). Modular Neural Networks. Proceedings IV National Conference on Methodsand Computer Systems in Research and Engineering Design. Kraków: Wyd. AG-H, Politechnika Krakowska, Uniwersytet Jagielloński.
• Wiśniewski, R. (2008). Multidimensional Forecasting of the Value of a Real Estate. Dissertationsand Monographs, No. 124, UWM.
• Wiśniewski, R. (2011). Modeling of Residential Property Prices Index Using Committees ofArtificial Neural Networks for PIGS, the European-G8, and Poland. Economic Modelling (sent to the publisher).
• Wolverton, M.L. & Senteza, J. (2000). Hedonic Estimates of Regional Constant Quality House Prices. Journal of Real Estate Research, 19 (3).
• Worzala, E., Lenk, M. & Silva, A. (1995). An Exploration of Neural Networks and Its Application to Real Estate Valuation. The Journal of Real Estate Research, Vol. 10, No. 2.

Identifiers

DOI

10.2478/v10031-012-0036-3