# RECORD 96e0ec640b350c1ec477b4a65c34f99c77d53503 # TITLE: Identifying Determinants of Residential Property Values in South London # YEAR: 2011.0 VENUE: nan DOI: 10.2478/v10135-011-0004-0 # AUTHORS: D. E. May; Adam R. Corbin; Philip D. Hollins # PAGES: 10 CANDIDATE TABLE PAGES (1-based): [5] # ABSTRACT: Identifying Determinants of Residential Property Values in South London This paper advances the academic work conducted on identifying some of the determinants of residential property values in different countries. Such determinants might include positive factors such as access to amenities, or negative factors such as high voltage overhead transmission lines (HVOTL). The objective explored here is to extend this research in the UK by considering the particular case of residential property values in South London. A number of determinants is considered, grouped and a model which explains the effect of the determinants on value is produced. The elasticities between property values and the distances from different determinants were also analysed to help highlight relationships between the determinants and residential property values. The results showed a relationship between residential property values and the determinants, and a relationship between the determinants themselves. ===== PAGE 1 (score 5, numeric-paren 0, table-words 0, var-words 5) ===== NÁRODOHOSPODÁŘSKÝ OBZOR – REVIEW OF ECONOMIC PERSPECTIVES, VOL. 11, ISSUE 1, 2011, pp. 3–11, DOI: 10.2478/v10135-011-0004-0 IDENTIFYING DETERMINANTS OF RESIDENTIAL PROPERTY VALUES IN SOUTH LONDON Daniel E. May, Adam R. Corbin and Philip D. Hollins Harper Adams University College, Newport, Shropshire, TF10 8NB, United Kingdom, dmay@harper-adams.ac.uk, adam.corbin@northumbria.ac.uk, phollins@harper- adams.ac.uk Abstract: This paper advances the academic work conducted on identifying some of the determinants of residential property values in different countries. Such determinants might include positive factors such as access to amenities, or negative factors such as high voltage overhead transmission lines (HVOTL). The objective explored here is to extend this research in the UK by considering the particular case of residential property values in South London. A number of determinants is considered, grouped and a model which explains the effect of the determinants on value is produced. The elasticities between property values and the distances from different determinants were also analysed to help highlight relationships between the determinants and residential property values. The results showed a relationship between residential property values and the determinants, and a relationship between the determinants themselves. Key words: House characteristics, amenity places, house value, modelling residential house price, cross elasticities JEL Classification: R21, R32 DOI: 10.2478/v10135-011-0004-0 ===== PAGE 5 (score 34, numeric-paren 5, table-words 9, var-words 6) ===== REVIEW OF ECONOMIC PERSPECTIVES 6 whether the multicollinearity problem was avoided using this strategy, collinearity was assessed using auxiliary regressions. According to Hill et al. (2008), detrimental collinearity is present when the coefficients of determination of these regressions are equal to or higher than 0.80. Because the coefficients of determination of the auxiliary regressions estimated in this research were lower than 0.80, it was concluded that detrimental collinearity was not present in the model. The auxiliary regressions are shown in Appendix B. Finally, The Goldfeld-Quandt test was used to test heteroscedasticity. For this purpose, the n observations in the sample were ordered by the magnitude of P and separate regressions were run for the first 469 and the last 469 observations. The residual sum of squares in the two sub-regressions was used to obtain the ratio test RSS2/RSS1 (i.e. the Goldfeld-Quandt test) where RSS1 and RSS2 are the residual sum of squares of the first and second sub-regression, respectively (see Dougherty, 2007, p. 229). The values of RSS1 and RSS2 obtained from the sub- regressions were 5.23 and 6.35, respectively. Using this test, heteroscedasticity was rejected at the 10% of significant level, but not at the 5%. As a consequence, the results obtained in this investigation have to be considered with caution. The estimation of the model described in Equation 1 is presented in Table 1, and the relevant elasticities are presented in Table 2. Table 1: Hedonic multiple regression estimation Variable Coefficient t-statistic p-value Intercept Terraced Semi-detached Leasehold Flat LnDCL LnDPK LnDRA LnDWS LnDRA*LnDPY LnDRA*LnDPK LnDWS*LnDPY LnDPY*LnDPK 9.54 -0.12 -0.08 -0.19 -0.33 0.03 1.08 0.87 -0.89 -0.05 -0.09 0.11 -0.08 12.26 -4.63 -3.48 -2.22 -373 3.63 5.98 5.53 -7.77 -5.37 -4.99 6.19 -6.10 0.000 0.000 0.005 0.027 0.002 0.003 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Adjusted R2 0.44 S.E. of regression 0.17 Table 2: Elasticities of house’s value and distance variables Elasticity Value ∂LnP/∂LnDCL = 0.03 ∂LnP/∂LnDPK = 1.08 – 0.09*LnDRA – 0.08*LnDPY ∂LnP/∂LnDRA = – 0.05*LnDPY – 0.09*LnDPK ∂LnP/∂LnDWS = – 0.89 + 0.11*LnDPY ===== PAGE 6 (score 16, numeric-paren 0, table-words 4, var-words 8) ===== Volume 11, Issue 1, 2011 7 The model presented in Table 1 reveals that property values are affected by different house characteristics. Leasehold was shown to have a negative effect on value, however this result has to be considered with caution as this indicator needs to be contextualised with the other characteristics and the data considered by the model. For example, it is possible to demonstrate that by keeping all the distance variables constant, a freehold flat is less expensive than leasehold semi-detached house. Whilst it is common sense that most flats are worth less than houses of any type; a freehold flat is most unusual, as it would have to be in contact with the ground to be registered as freehold. Therefore, a clear picture of the way in which house characteristics affect property values depends upon how these characteristics are combined, and the legal realities of home ownership in England and Wales. Table 2, on the other hand, shows that house values increases by 0.03% when the distance to the centre line of HVOLT (DCL) increases by 1%. This result is consistent with that obtained by Sims et al. (2009) and Sims and Dent (2005). That is, this evidence supports the existence of a relationship between value diminution and the presence of an HVOTL near residential properties. Table 2 also shows that the relationship between proximity to public parks (DPK) and property values is relative as it depends on the interaction between proximity to public parks, railway lines (DRA) and pylons (DPY). For example, the elasticity between house values and DPK is equal to -0.16 for houses located 1,500 metres from railway lines and pylons. For this set of houses, proximity to public parks increases the value of residential properties. In contrast, the elasticity for houses located 500 metres from these infrastructures is 0.02 indicating that proximity to public parks decreases the property value for this set of houses. It could be inferred from this result that the beneficial effects of proximity to a public park disappears in the presence of railway lines and pylons. This finding is consistent with that obtained by Giaccaria and Frontuto (2007). In synthesis, residents in South London did not consider public parks as an isolate amenity attribute when purchasing residential properties but as an element within a more complex system that involves a number of interactions. A similar result has been observed in the case of proximity to railway lines; here the way in which they affect residential property values depends upon the interaction between the lines and the proximity to pylons and public parks. Finally, Table 2 shows that an interaction exists between proximity to Welling station (DWS) and pylons. For example, the elasticity between house values and DWS is equal to 0.01 for houses located 3,500 metres from pylons. In contrast, this elasticity is equal to -0.21 for houses located 500 metres from the pylons. This finding suggests that any loss of value caused by the presence of pylons is partially offset when a transport service is available. Conclusions The present investigation used a hedonic model to identify the effect of a number of determinants on residential property values in South London. The results revealed that these values were negatively affected by closeness to HVOTL and by house attributes such as where they were part of a terrace. In contrast, detached houses had higher value. The existence of an interaction was also found between some determinants, which resulted in an ambiguous effect upon house values. For example, proximity to public parks had only a positive effect on property values for houses located far away from