Determinants of house prices in Hout Bay

[摘要] ENGLISH ABSTRACT: The research problem addressed in this study is how to ascertain the primary determinants ofhouse prices in Hout Bay. This overarching aim encompasses three interwoven aspects. Theresearch attempts first to determine which factors generally affect property prices in HoutBay; second, to assess the extent to which individual factors affect house prices; and third, todiscover the role variables collectively play in determining house prices in Hout Bay. Fourobjectives emerge from this subdivision of the aim, namely identify potential house priceinfluencingfactors in Hout Bay; quantify the selected locational variables; statisticallyanalyse the variables to distinguish the significant and insignificant ones; and use regressionanalysis to deduce the collective and individual influences of the significant factors on houseprices.Structured interviews were conducted with representatives of 12 estate agencies in Hout Bayto uncover factors affecting the local property market. Through insights gleaned from theliterature, manipulation of municipal valuation and cadastral data and the structuredinterviews, 39 structural and site-related variables, 18 distance variables and 11 socioeconomicvariables were constructed.Several preliminary and descriptive analyses performed on the variables gave a generalimpression of the distribution of data and assisted in identifying statistically significantvariables for determining house prices. These analyses included measures of central tendency(mean, median and mode); measures of dispersion (minimum and maximum values, range,standard deviation, skewness and kurtosis); the compilation of histograms for each variable;analysis of variance (ANOVA) on nominal data variables; and the creation of 2D scatterplotsfor ordinal data variables. Spearman rank order correlation was performed on the nominal andordinal data variables. Statistically weak variables and those exhibiting signs ofmulticollinearity were eliminated. A best-subsets regression analysis was executed on theremaining variables.The regression model performed adequately, explaining close to 54% of the variation in houseprices in Hout Bay. Among the individual factors, the size of the erf was the strongestpredictor of the house price dependent variable, house size was the second most importantfactor, while distance to busy roads and quality of the house shared similar importance.Regression residuals were also mapped to expose spatial patterns. It is recommended thatcomparable research be conducted on a citywide scale, that variables be quantified differentlyand that new GIS techniques be incorporated in future studies.

[发布机构] Stellenbosch University