Application of regression techniques to capture value influences for mass valuation of residential property: <i>A case study of two residential estates in Nairobi </i>

This research project sets out to apply statistical techniques in the valuation of land and properties through various models. It focused on comparing predictive accuracies of mass valuation models with a dataset of 500 single-family property transactions in two neighbourhoods within Nairobi city. There are a number of statistical models that are used for mass valuation of properties. The first step in this study was to gather data on property sales used in the development of a base model and proposed model. Each of the property units in the database were geocoded and vectorized. The data was screened and visualized to investigate the nature of the potential association between the response, Y, and predictor variables, X. The predictor variables were tested for multicollinearity and a regression model developed based on hypothesized relationships. The model was tested for lack of fit by ordering the residuals using a residual scatterplots and histograms. Finally, the fitness statistics were reviewed by looking at the spread of the plot and evaluating observed values around the regression line, and examining how accurate the independent variables are in predicting the dependent variables. The results revealed an overall level of 0.96 for Komarock and 0.98 for Runda estate respectively. One measure of how well the model predicts is to compute the correlation between the actual values in the holdout sample and the predicted values. The correlation should be high when the model is valid. The correlation between the assessed value and the actual selling price is 0.71 and 0.98 for Komarock and Runda respectively. Determining the quality of the valuation output also requires measuring uniformity: uniformity between groups of properties and uniformity within groups (Abidoye, Huang, Amidu, & Javad, 2021). The coefficient of dispersion (COD) is the most used measure of valuation uniformity. The results show a COD of 18% and 10% for Komarock and Runda respectively.

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Publication Details

Journal
Journal of Agriculture Science and Technology
Published
2025-04-14
DOI
https://doi.org/10.4314/jagst.v24i1.11
Primary Topic
Housing Market and Economics
Type
article
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article

Application of regression techniques to capture value influences for mass valuation of residential property: A case study of two residential estates in Nairobi

Bernard Ochieng
Journal of Agriculture Science and Technology
Housing Market and Economics
article

Application of regression techniques to capture value influences for mass valuation of residential property: A case study of two residential estates in Nairobi

Bernard Ochieng
article en

Abstract

This research project sets out to apply statistical techniques in the valuation of land and properties through various models. It focused on comparing predictive accuracies of mass valuation models with a dataset of 500 single-family property transactions in two neighbourhoods within Nairobi city. There are a number of statistical models that are used for mass valuation of properties. The first step in this study was to gather data on property sales used in the development of a base model and proposed model. Each of the property units in the database were geocoded and vectorized. The data was screened and visualized to investigate the nature of the potential association between the response, Y, and predictor variables, X. The predictor variables were tested for multicollinearity and a regression model developed based on hypothesized relationships. The model was tested for lack of fit by ordering the residuals using a residual scatterplots and histograms. Finally, the fitness statistics were reviewed by looking at the spread of the plot and evaluating observed values around the regression line, and examining how accurate the independent variables are in predicting the dependent variables. The results revealed an overall level of 0.96 for Komarock and 0.98 for Runda estate respectively. One measure of how well the model predicts is to compute the correlation between the actual values in the holdout sample and the predicted values. The correlation should be high when the model is valid. The correlation between the assessed value and the actual selling price is 0.71 and 0.98 for Komarock and Runda respectively. Determining the quality of the valuation output also requires measuring uniformity: uniformity between groups of properties and uniformity within groups (Abidoye, Huang, Amidu, & Javad, 2021). The coefficient of dispersion (COD) is the most used measure of valuation uniformity. The results show a COD of 18% and 10% for Komarock and Runda respectively.

Journal of Agriculture Science and TechnologyVol. 24(1)
Openalex Percentile: Top 85%
Housing Market and Economics
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Application of regression techniques to capture value influences for mass valuation of residential property: <i>A case study of two residential estates in Nairobi </i> — Bernard Ochieng · Journal of Agriculture Science and Technology (2025) | TGRS Research Map | TGRS