Volume 8 - Number 2 | July 2024

A model of regional housing markets in England and Wales

Arnab Bhattacharjee, Chris Jensen-Butler

Abstract:

Purpose
We propose an economic model of housing markets. The model incorporates the macroeconomic relationships between prices, demand and supply. Since vacancy rates are not observable, the demand-supply mismatches are identified using a microeconomic model of search, matching and price formation. The model is applied to data on regional housing markets in England and Wales.

Design/methodology/approach
Economic theory combining macroeconomics and microeconomics together with new generation econometric methods for empirical analysis.

Findings
The empirical model, estimated for the ten government office regions of England and Wales, validates the economic model. We find that there is substantial heterogeneity across the regions, which is useful in informing housing and land-use policies. In addition to heterogeneity, the model enables us to better understand unrestricted inter-regional spatial relationships. The estimated spatial autocorrelations imply different drivers of spatial diffusion in different regions.

Research limitations/implications
In the nature of other empirical work, the findings are subject to specificities of the data considered here. The understanding of spatial diffusion can also be further developed in future work.

Practical implications
This paper develops a nice way of closing macroeconomic models of housing markets when complete demand, supply and pricing data are not available. The model may also be useful when data are available but with large measurement errors. The model comes together with corresponding empirical methods.

Social implications
Implications for the housing market and other regional policies are important. These are context-specific, but some implications for housing policy in the UK are provided in the paper as an example.

Originality/value
Unique housing market paper combining both macroeconomic and microeconomic theory as well as both theory and empirics. The rich framework so developed can be extended to much future work.

References:

  1. Anglin, P.M., Rutherford, R. and Springer, T.M. (2003), “The trade-off between selling price of residential properties and time-on-the-market: the impact of price setting”, Journal of Real Estate Finance and Economics, Vol. 26 No. 1, pp. 95-111, doi: 10.1023/a:1021526332732.
  2. Anselin, L. (1988), Spatial Econometrics: Methods and Models, Kluwer Academic, Dordrecht, The Netherlands.
  3. Anselin, L. (1999), “Spatial econometrics”, in Baltagi, B.H. (Ed.), A Companion to Theoretical Econometrics, Basil Blackwell, Oxford, pp. 310-330.
  4. Anselin, L. (2002), “Under the hood: issues in the specification and interpretation of spatial regression models”, Agricultural Economics, Vol. 27 No. 3, pp. 247-267, doi: 10.1111/j.1574-0862.2002.tb00120.x.
  5. Anselin, L. and Lozano-Gracia, N. (2009), “Spatial hedonic models”, in Mills, T.C. and Patterson, K. (Eds), Palgrave Handbook of Econometrics, Volume 2: Applied Econometrics, Palgrave Macmillan, London, UK, pp. 1213-1250, Chapter 26.
  6. Anselin, L., Lozano-Gracia, N., Deichmann, U. and Lall, S. (2010), “Valuing access to water – a spatial hedonic approach, with an application to Bangalore, India”, Spatial Economic Analysis, Vol. 5 No. 2, pp. 161-179, doi: 10.1080/17421771003730703.
  7. Arnold, M.A. (1999), “Search, bargaining and optimal asking prices”, Real Estate Economics, Vol. 27 No. 3, pp. 453-482, doi: 10.1111/1540-6229.00780.
  8. Ashworth, J. and Parker, S. (1997), “Modelling regional house prices in the UK”, Scottish Journal of Political Economy, Vol. 44 No. 3, pp. 225-246, doi: 10.1111/1467-9485.00055.
  9. Baltagi, B., Song, S.H. and Koh, W. (2003), “Testing panel data regression models with spatial error correlation”, Journal of Econometrics, Vol. 117 No. 1, pp. 123-150, doi: 10.1016/s0304-4076(03)00120-9.
  10. Barker, K. (2003), Review of Housing Supply, Interim Report, HM Treasury, London.
  11. Barker, K. (2004), Review of Housing Supply, Final Report, HM Treasury, London.
  12. Bhattacharjee, A. and Jensen-Butler, C. (2013), “Estimation of the spatial weights matrix under structural constraints”, Regional Science and Urban Economics, Vol. 43 No. 4, pp. 617-634, doi: 10.1016/j.regsciurbeco.2013.03.005.
  13. Bhattacharjee, A., Castro, E., Maiti, T. and Marques, J. (2016), “Endogenous spatial regression and delineation of submarkets: a new framework with application to housing markets”, Journal of Applied Econometrics, Vol. 31 No. 1, pp. 32-57, doi: 10.1002/jae.2478.
  14. Bhattacharjee, A., Cai, L. and Maiti, T. (2017), “Functional regression over irregular domains: variation in the shadow price of living space”, Spatial Economic Analysis, Vol. 12 Nos 2-3, pp. 182-201, doi: 10.1080/17421772.2017.1286374.
  15. Bhattacharjee, A., Ditzen, J. and Holly, S. (2022), “Spatial and spatio-temporal error correction, networks and common correlated effects”, in Chudik, A., Hsiao, C. and Timmermann, A. (Eds), Essays in Honor of M. Hashem Pesaran: Panel Modeling, Micro Applications, and Econometric Methodology’, Advances in Econometrics, Vol. 43B, Emerald Publishing, Leeds, pp. 37-60, Chapter 2.
  16. Bhattacharjee, A., Castro, E. and Marques, J.L. (2024), “Modelling spatial urban housing markets”, in Gibb, K., Leishman, C., Marsh, A., Meen, G., ViforJ, R.O. and Watkins, C. (Eds), The‘Routledge Handbook of Housing Economics’, Routledge, pp. 27-42, Chapter 3.
  17. Bound, J., Jaeger, D. and Baker, R. (1995), “Problems with instrumental variables estimation when the correlation between instruments and the endogenous explanatory variable is weak”, Journal of American Statistical Association, Vol. 90 No. 430, pp. 443-450, doi: 10.1080/01621459.1995.10476536.
  18. Cai, L., Bhattacharjee, A., Calantone, R. and Maiti, T. (2019), “Variable selection with spatially autoregressive errors: a generalized moments LASSO estimator”, Sankhyā B, Vol. 81 No. 1, pp. 146-200, doi: 10.1007/s13571-018-0176-z.
  19.  
  20. Cameron, G. and Muellbauer, J. (1998), “The housing market and regional commuting and migration choices”, Scottish Journal of Political Economy, Vol. 45 No. 4, pp. 420-446, doi: 10.1111/1467-9485.00106.
  21. Cheshire, P. and Sheppard, S. (2004), “Capitalising the value of free schools: the impact of supply constraints and uncertainty”, The Economic Journal, Vol. 114 No. 499, pp. F397-F424, doi: 10.1111/j.1468-0297.2004.00252.x.
  22. Cook, S. (2003), “The convergence of regional house prices in the UK”, Urban Studies, Vol. 40 No. 11, pp. 2285-2294, doi: 10.1080/0042098032000123295.
  23. Cook, S. and Holly, S. (2000), “Statistical properties of UK house prices: an analysis of disaggregated vintages”, Urban Studies, Vol. 37 No. 11, pp. 2045-2055, doi: 10.1080/713707230.
  24. Elhorst, J.P. (2003), “Specification and estimation of spatial panel data models”, International Regional Science Review, Vol. 26 No. 3, pp. 244-268, doi: 10.1177/0160017603253791.
  25. Favilukis, J., Ludvigson, S.C. and Van Nieuwerburgh, S. (2017), “The macroeconomic effects of housing wealth, housing finance, and limited risk sharing in general equilibrium”, Journal of Political Economy, Vol. 125 No. 1, pp. 140-223, doi: 10.1086/689606.
  26. Fiebig, D.G. (1999), “Seemingly unrelated regression”, in Baltagi, B.H. (Ed.), A Companion to Theoretical Econometrics, Basil Blackwell, Oxford, pp. 101-121.
  27. Fuerst, F. (2004), “Forecasting the Manhattan office market with a simultaneous equation model”, Working Paper, Center for Urban Research, City University of New York.
  28. Giacomini, R. and Granger, C.W.J. (2004), “Aggregation of space-time processes”, Journal of Econometrics, Vol. 118 Nos 1-2, pp. 7-26, doi: 10.1016/s0304-4076(03)00132-5.
  29. Gibbons, S. (2004), “The costs of urban property crime”, The Economic Journal, Vol. 114 No. 499, pp. F441-F463, doi: 10.1111/j.1468-0297.2004.00254.x.
  30. Gibbons, S. and Machin, S. (2003), “Valuing English primary schools”, Journal of Urban Economics, Vol. 53 No. 2, pp. 197-219, doi: 10.1016/s0094-1190(02)00516-8.
  31. Gibbons, S. and Machin, S. (2005), “Valuing rail access using transport innovations”, Journal of Urban Economics, Vol. 57 No. 1, pp. 148-169, doi: 10.1016/j.jue.2004.10.002.
  32. Hendershott, P. (1996), “Rental adjustment and valuation in overbuilt markets: evidence from the Sydney office market”, Journal of Urban Economics, Vol. 39 No. 1, pp. 51-67, doi: 10.1006/juec.1996.0003.
  33. Hendershott, P., MacGregor, B. and White, M. (2002), “Explaining real commercial rents using an error correction model with panel data”, Journal of Real Estate Finance and Economics, Vol. 24, pp. 59-87, doi: 10.1007/978-1-4757-5988-4_4.
  34. Holmans, A.E. (1990), “House prices: changes through time at national and sub-national level”, Government Economic Service Working Paper No. 110, HMSO, London.
  35. Hsieh, T.-C. and Moretti, E. (2019), “Housing constraints and spatial misallocation”, American Economic Journal: Macroeconomics, Vol. 11 No. 2, pp. 1-39, doi: 10.1257/mac.20170388.
  36. Kelejian, H.H. and Prucha, I.R. (2004), “Estimation of simultaneous systems of spatially interrelated cross sectional equations”, Journal of Econometrics, Vol. 118 Nos 1-2, pp. 27-50, doi: 10.1016/s0304-4076(03)00133-7.
  37. Krainer, J. (2001), “A theory of liquidity in residential real estate markets”, Journal of Urban Economics, Vol. 49 No. 1, pp. 32-53, doi: 10.1006/juec.2000.2180.
  38. Maclennan, D. and Long, J. (2024), “A missing perspective in housing economics: productivity?”, in Gibb, K., Leishman, C., Marsh, A., Meen, G., ViforJ, R.O. and Watkins, C. (Eds), The‘Routledge Handbook of Housing Economics’, Routledge, pp. 149-162, Chapter 12.
  39. Meen, G. (1996), “Ten propositions in UK housing macroeconomics: an overview of the 1980s and early 1990s”, Urban Studies, Vol. 33 No. 3, pp. 425-444, doi: 10.1080/00420989650011843.
  40. Meen, G. (1999), “Regional house prices and the ripple effect: a new interpretation”, Housing Studies, Vol. 14 No. 6, pp. 733-753, doi: 10.1080/02673039982524.
  41. Meen, G. (2001), Modelling Spatial Housing Markets: Theory, Analysis and Policy, Kluwer Academic, Boston.
  42. Meen, G. (2003), “Regional housing supply elasticities in England”, Report prepared for the barker review of housing supply, Centre for Spatial and Real Estate Economics, University of Reading, October 2003.
  43. Meen, G. (2008), “Ten new propositions in UK housing macroeconomics: an overview of the first years of the century”, Urban Studies, Vol. 45 No. 13, pp. 2759-2781, doi: 10.1177/0042098008098205.
  44. Meen, D. and Meen, G. (2003), “Social behaviour as a basis for modelling the urban housing market: a review”, Urban Studies, Vol. 40 Nos 5-6, pp. 917-935, doi: 10.1080/0042098032000074245.
  45. Muellbauer, J. (2003), “Housing, credit and the Euro: the Policy response”, Report prepared for the Barker Review of Housing Supply.
  46. Muellbauer, J. and Murphy, A. (1997), “Booms and busts in the UK housing market”, The Economic Journal, Vol. 107 No. 445, pp. 1720-1746, doi: 10.1111/1468-0297.00251.
  47. Pesaran, M.H. (2015), “Testing weak cross-sectional dependence in large panels”, Econometric Reviews, Vol. 34 Nos 6-10, pp. 1089-1117, doi: 10.1080/07474938.2014.956623.
  48. Pesaran, M.H. and Smith, R.P. (1995), “Estimating long-run relationships from dynamic heterogeneous panels”, Journal of Econometrics, Vol. 68 No. 1, pp. 79-113, doi: 10.1016/0304-4076(94)01644-f.
  49. Pesaran, M.H. and Tosetti, E. (2011), “Large panels with common factors and spatial correlation”, Journal of Econometrics, Vol. 161 No. 2, pp. 182-202, doi: 10.1016/j.jeconom.2010.12.003.
  50. Rosenthal, S. (1999), “Residential buildings and the cost of construction: new evidence from the housing market”, Review of Economics and Statistics, Vol. 81 No. 2, pp. 288-302, doi: 10.1162/003465399558085.
  51. Wheaton, W.C. (1990), “Vacancy, search and prices in a housing market matching model”, Journal of Political Economy, Vol. 98 No. 6, pp. 1270-1292, doi: 10.1086/261734.
  52. Wheaton, W.C. and Torto, R.G. (1993), “Office rent indices and their behavior over time”, Journal of Urban Economics, Vol. 35 No. 2, pp. 112-139, doi: 10.1006/juec.1994.1008.
  53. Wheaton, W.C., Torto, R.G. and Evans, P. (1997), “The cyclic behavior of the Greater London office market”, Journal of Real Estate Finance and Economics, Vol. 15 No. 1, pp. 77-92, doi: 10.1023/a:1007701422238.
  54. White, M. (2024), “Housing market liquidity”, in Gibb, K., Leishman, C., Marsh, A., Meen, G., ViforJ, R.O. and Watkins, C. (Eds), The‘Routledge Handbook of Housing Economics’, Routledge, pp. 133-148, Chapter 11.
  55. Yavas, A. (1992), “A simple search and bargaining model of real estate markets”, Journal of American Real Estate and Urban Economics Association, Vol. 20 No. 4, pp. 533-548, doi: 10.1111/1540-6229.00595.
  56. Zellner, A. (1962), “An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias”, Journal of the American Statistical Association, Vol. 57 No. 298, pp. 348-368, doi: 10.2307/2281644.

JEL classification: R21,R31,R33,C31.