The hedonic price model is generally used to estimate the effects of non-market amenities and disamenities on adjacent property values (Rosen, 1974). The source of data on housing prices is an important theoretical consideration to take into account when deciding on the appropriate dependent variable to use for a hedonic price analysis, with data on actual market transactions being preferable, as opposed to assessed values (Kiel & Zabel, 1999; Cotteleer & van Kooten, 2012; Ma & Swinton, 2012). The contribution of this study is to provide a complete analysis and statistically test for differences in parameter estimates obtained from assessed values versus actual sales prices. To achieve this, a Seemingly Unrelated Regression (SUR) hedonic price model consisting of two equations was estimated – one with municipal assessed values as the dependent variable and the other employing actual sales prices as the dependent variable. The resulting parameter estimates were then compared. In order to answer the research question, 170 residential properties in the neighbourhood of Walmer, Nelson Mandela Bay were analysed in terms of the influence of select structural and neighbourhood characteristics on both the sales prices and the assessed values. More specifically, each hedonic equation consisted of nine structural housing characteristics and two neighbourhood characteristics.

The majority of the property characteristics included in the estimated SUR model displayed the correct signs, with the exception of the age (age of the house) variable (positive sign) and the number of bedrooms (negative sign). A possible explanation for the positive sign on the age coefficient in both equations is the fact that the Walmer neighbourhood is one of Nelson Mandela Bay’s oldest and most affluent suburbs. Buyers perhaps prefer the older, more traditional homes in the suburb. The other perplexing finding was the negative sign on the bed (number of bedrooms) coefficient. One would expect house prices and assessed values to be positively related to the number of bedrooms. The results of this study show the opposite. Perhaps more value is placed on the size of the bedrooms as opposed to the number. The influence of the other variables on house prices and assessed values was fairly predictable. Properties situated on bigger erfs were valued more highly than properties situated on smaller erfs. The number of stories, number of bathrooms, the presence of a swimming pool, the presence of an air-conditioner, the presence of a garage and the presence of an electric fence all had positive influences on both house prices and assessed values.

The results of this study rejected the hypothesis that the coefficient estimates for house characteristics generated by a hedonic price model using assessed values as the dependent variable are similar to those estimated using the actual sales price as the dependent variable. In addition to this, clear differences exist between the distributions of actual sales prices and assessed values, with actual sales prices being, on average, lower than assessed values. This does not necessarily imply that they are different in an economically relevant manner (which would be of interest to policy makers). However, although there are clear data advantages to using assessed values as the dependent variable, actual sales prices reflect true market conditions more accurately than assessed values. Economic intuition, thus, suggests that actual sales prices are preferred to assessed values. The central policy implication resulting from the study is that hedonic practitioners should exercise caution when selecting the dependent variable for hedonic studies. Ideally, the use of assessed values as the dependent variable should be avoided if actual transaction data are available.