Thesis (MA (Geography and Environmental Studies))--University of Stellenbosch, 2010. / ENGLISH ABSTRACT: The research problem addressed in this study is how to ascertain the primary determinants of house prices in Hout Bay. This overarching aim encompasses three interwoven aspects. The research attempts first to determine which factors generally affect property prices in Hout Bay; second, to assess the extent to which individual factors affect house prices; and third, to discover the role variables collectively play in determining house prices in Hout Bay. Four objectives emerge from this subdivision of the aim, namely identify potential house priceinfluencing factors in Hout Bay; quantify the selected locational variables; statistically analyse the variables to distinguish the significant and insignificant ones; and use regression analysis to deduce the collective and individual influences of the significant factors on house prices. Structured interviews were conducted with representatives of 12 estate agencies in Hout Bay to uncover factors affecting the local property market. Through insights gleaned from the literature, manipulation of municipal valuation and cadastral data and the structured interviews, 39 structural and site-related variables, 18 distance variables and 11 socioeconomic variables were constructed. Several preliminary and descriptive analyses performed on the variables gave a general impression of the distribution of data and assisted in identifying statistically significant variables 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 scatterplots for ordinal data variables. Spearman rank order correlation was performed on the nominal and ordinal data variables. Statistically weak variables and those exhibiting signs of multicollinearity were eliminated. A best-subsets regression analysis was executed on the remaining variables. The regression model performed adequately, explaining close to 54% of the variation in house prices in Hout Bay. Among the individual factors, the size of the erf was the strongest predictor of the house price dependent variable, house size was the second most important factor, 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 that comparable research be conducted on a citywide scale, that variables be quantified differently and that new GIS techniques be incorporated in future studies. / AFRIKAANSE OPSOMMING: Die navorsingsprobleem wat hierdie studie aanspreek, is hoe om vas te stel wat die primêre faktore is wat huispryse in Houtbaai bepaal. Hierdie oorkoepelende doelwit vervat drie onderling verwante aspekte. Eerstens, poog die navorsing om te bepaal watter faktore in die algemeen huispryse in Houtbaai beïnvloed; tweedens, om te assesseer tot watter mate individuele faktore huispryse affekteer; en derdens, om te ontdek watter kollektiewe rol veranderlikes in die bepaling van huispryse in Houtbaai speel. Vanuit hierdie onderverdeling van die navorsingsdoelwit het vier doelstellings ontstaan, naamlik identifiseer die potensiële faktore wat huispryse in Houtbaai beïnvloed; kwantifiseer die geselekteerde liggingsveranderlikes; voer verskeie analises uit op die veranderlikes om die beduidende en onbeduidende veranderlikes te identifiseer; en benut regressie-analise om die kollektiewe en individuele invloed van beduidende faktore op huispryse in die studiegebied vas te stel. Gestruktureerde onderhoude is met verkoopslui van 12 eiendomsagentskappe in Houtbaai gevoer om die faktore te bepaal wat die plaaslike eiendomsmark beïnvloed. Deur middel van insigte verkry uit die akademiese literatuur, manipulasie van munisipale waardasie- en kadastrale data en die gestruktureerde onderhoude is 39 strukturele en liggingsverwante veranderlikes, 18 afstandsveranderlikes en 11 sosio-ekonomiese veranderlikes geskep. Verskeie analises wat op die veranderlikes uitgevoer is, het ‘n algemene indruk van die verspreiding van die data verskaf en het die identifisering van statistiesbeduidende veranderlikes bevorder. Hierdie analises het maatstawwe vir sentrale neiging (rekenkundige gemiddelde, mediaan en modus); maatstawwe vir dispersie (minimum en maksimum, variasiewydte, standaardafwyking, skeefheid en kurtose); die samestelling van histogramme vir elke veranderlike; die analise van variansie (ANOVA) op veranderlikes met nominale data; en die skep van 2D-spreidingstippe vir veranderlikes met ordinale data behels. Spearman se rangorde korrelasie is op beide die nominale en ordinale data uitgevoer. Statistiesonbeduidende veranderlikes, of dié wat tekens van multikollineariteit met ander veranderlikes getoon het, is geëlimineer. ‘n Beste deelversameling regressie-analise is uitgevoer op die oorblywende veranderlikes. Die regressiemodel het gepaste resultate behaal deurdat dit byna 54% van die variasie in Houtbaai se huispryse verklaar het. Van die individuele veranderlikes was die grootte van die erf die sterkste voorspeller van die huisprys afhanklike veranderlike, huisgrootte was die tweede belangrikste faktor, terwyl afstand van besige paaie en die kwaliteit van die huis soortgelyke invloed gedeel het. Die regressiemodel se residu’s is gekarteer om ruimtelike patrone vas te stel. Dit word aanbeveel dat soortgelyke navorsing op ‘n stadswye skaal uitgevoer word, dat die veranderlikes op ander wyses gekwantifiseer word en dat nuwe GIStegnieke in toekomstige studies aangewend word.