Modelling and Forecasting Property Types’ Price Changes and Correlations within the City of Manchester, UK
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Most of the research done on real estate markets to date has concentratedon aggregate real estate price indices and correlations between regional propertiesassets. Previous research also shows that the residential real estate market is lessstudied compared to commercial real estate despite figures showing huge potentialgrowth in the residential real estate market. This paper covers residential real estatemarkets by property types (flats, terraced, semi-detached, and detached) within thecity of Manchester, UK. The paper covers their time series properties as well astheir correlations. The data period is divided into estimation sample from 1995 to2011 and forecasting sample from 2011 to 2013.The highest risk per one percent ofreturn as indicated by the coefficient of variation is for detached properties followedby terraced, flats and semi-detached properties. Property types correlations showthat the highest correlation is between the most expensive properties, detached andsemi-detached and the next highest correlations are between the less expensive,terraced and flats due to the close substitution of those property types. The pricedecline for detached property took year to show positive price change while forflats and terraced properties it only took a quarter to show a positive price changes.
Metrics
##plugins.themes.bootstrap3.article.details##
Estate PricesInvestment by Property TypesForecasting Real Estate
• Birch, J. W. and Sunderman, M. A. (2003), “Estimating price paths for residential real estate”, Journal of Real Estate Research, 25(3), 277-300.
• Box, G. and Jenkins, G. (1974), “Time series analysis: Forecasting and control”.
• Brocks, C. and Tsolacos, S. (1999), “The impact of economic and financial factors on UK property performance”, Journal of Property Research 16(2), 139-52.
• Case, K. E. and Shiller, R. J. (1990), “Returns and risk on real estate and other investments: More evidence”, Journal of Real Estate Portfolio Management, Vol.8, No. 3: 265-279.
• Clements, M. P. and Hendry, D. F. (1996), “Intercept corrections and structural change”, Journal of Applied Econometrics, 11, 475-94.
• Crawford, G. and Fratantoni, M. (2003), “Assessing the forecasting performance of regimeswitching, ARIMA and GARCH models of house prices”, Real Estate Economics, 31, 223-43.
• Gallin, J. (2006),“The long-run relationship between house prices and income: Evidence from local housing markets” Real Estate Economics, 34(3):417–438.
• Glaeser, E. L. and Gyourko, J. (2006), “Housing dynamics”, National Bureau of Economic Research.
• Gooijer J. G. and Hyndman R. J. (2006), “25 years of time series forecasting”, International Journal of Forecasting, 443-473.
• Hepsen, A. and Vatansever, M. (2010), “Forecasting future trends in Dubai housing market by Using Box-Jenkins Autoregressive Integrated Moving Average International”, Journal of Housing Markets and Analysis, 4(3), 210-223.
• Hirotugu, A. (1974), “A new look at the statistical model identification”,IEEE Transactions on Automatic Control 19 (6): 716–723.
• Malpezzi, S. (1999), “A simple error correction model of house prices”,Journal of Housing Economics, 8(1): 27-62.
• Meese, R. and Wallace, N. (1991), “Nonparametric estimation of dynamic hedonic price models and the construction of residential housing price indices”. Real Estate Economics, 19(3): 308-332.
• Mehmedovic, M. H., Arslanagic, M., Kadic, S. and Cicic, M. (2010), “Decision making in real estate purchase the case of bosnia-herzegovina”.
• Muellbauer, J. and Murphy, A. (1997), “Booms and busts in the UK housing market”, The Economic Journal, 107:1701-1727(27).
• Pagourtzi , E., Assimakopoulos, V., Hatzichristos and French, N. (2003), “Real estate appraisal: a review of valuation methods”, Journal of Property Investment and Finance, 21,4.
• Sklarz, M. A., Miller, N. G. and Gersch, W. (1987), “Forecasting using long order autoregressive processes: An example using housing starts”,Real Estate Economics, 154(4), 374-388.
• Stevenson, S. (2007), “A comparison of the forecasting ability of ARIMA models”, Journal of Property Investment & Finance, 25(3), 223-240.
• Tse, R. (1997), “An application of the ARIMA model to real-estate prices in Hong Kong”, Journal of Property Finance, 8(2), 152-163.
• Vishwakarma, V. K. (2013), “Forecasting real estate business: empirical evidence from the Canadian market”, Global Journal of Business Research, Vol.7, No.3.
• Worthington, A. C. and Higgs, H. (2003), “Co-movements in UK regional property markets: a multivariate co-integration analysis”,Journal of Property Investment & Finance, 2003; 21,4. www.landregistry.gov.uk Accessed August 2011.