1# RECORD 9dd3ef8baea1541e7438928532ebd498c89ffa882# TITLE: Hedonic Valuation of Real Estate Properties in Nigeria, Hedonic Valuation of Properties in Nigeria3# YEAR: 2018.0 VENUE: Journal of African Real Estate Research DOI: 10.15641/jarer.v1i1.4524# AUTHORS: R. Abidoye; A. Chan5# PAGES: 19 CANDIDATE TABLE PAGES (1-based): [10]6# ABSTRACT: Despite the wide adoption of the Hedonic Pricing Model (HPM) approach in property valuation in the real estate domain, a large number of studies conducted by real estate scholars, have tended to focus on the ‘explanatory’ rather than the ‘predictive’ ability of the technique. Given this industry focus, and thus the lack of information on the predictive nature of HPM, the present study sets out to investigate the predictive accuracy of HPM in property valuation. Data on sales transaction of residential properties were collected from registered property firms practising in the Lagos metropolis property market, in Nigeria. The collected data were divided into two categories - the training set which was fitted to the HPM developed, and the testing dataset which was used for the model validation. Accuracy metrics were used to evaluate the predictive accuracy of the HPM developed. The analysis shows that the HPM approach produced inaccurate predicted property values in terms of a high mean absolute percentage error (MAPE), mean absolute error (MAE) and root means square error (RMSE) which may not be acceptable by rational real estate investors. Also, about 60% of the predicted property values produced an inaccuracy that is ± 20% of the actual values, while only 26% yielded an error of margin which is within the industry acceptable margin of between ± 0 and 10%. The HPM could not be entirely relied on to produce accurate property valuation estimates. Therefore, further studies are needed to be carried out to identify more reliable and robust property valuation approaches that can produce values that would be acceptable to all property valuation stakeholders.789===== PAGE 1 (score 4, numeric-paren 0, table-words 0, var-words 4) =====10111 rotimi.abidoye@connect.polyu.hk; +85234008113 12 13Journal of African Real Estate Research 14Volume 3, Issue 1 15www.journals.uct.ac.za/index.php/JARER/index 16 17 18Hedonic Valuation of Real Estate Properties in Nigeria 19 20Rotimi Boluwatife Abidoye1, and Albert P. C. Chan2 21 22 231,2 Department of Building and Real Estate, The Hong Kong Polytechnic University, Hung 24Hom, Hong Kong 25 26 27To cite this article: Abidoye, R. B., & Chan, A. P. C. (2018). Hedonic Valuation of Real Estate 28Properties in Nigeria. Journal of African Real Estate Research, 3(1), pp.122-140. DOI: 2910.15641/jarer.v1i1.452. 30 31Abstract 32 33Despite the wide adoption of the Hedonic Pricing Model (HPM) approach in property valuation 34in the real estate domain, a large number of studies conducted by real estate scholars, have 35tended to focus on the ‘explanatory’ rather than the ‘predictive’ ability of the technique. Given 36this industry focus, and thus the lack of information on the predictive nature of HPM, the 37present study sets out to investigate the predictive accuracy of HPM in property valuation. Data 38on sales transaction of residential properties were collected from registered property firms 39practising in the Lagos metropolis property market, in Nigeria. The collected data were divided 40into two categories - the training set which was fitted to the HPM developed, and the testing 41dataset which was used for the model validation. Accuracy metrics were used to evaluate the 42predictive accuracy of the HPM developed. The analysis shows that the HPM approach 43produced inaccurate predicted property values in terms of a high mean absolute percentage 44error (MAPE), mean absolute error (MAE) and root means square error (RMSE) which may 45not be acceptable by rational real estate investors. Also, about 60 % of the predicted property 46values produced an inaccuracy that is ± 20% of the actual values, while only 26% yielded an 47error of margin which is within the industry acceptable margin of between ± 0 and 10%. The 48HPM could not be entirely relied on to produce accurate property valuation estimates. 49Therefore, further studies are needed to be carried out to identify more reliable and robust 50property valuation approaches that can produce values that would be acceptable to all property 51valuation stakeholders. 52 53Keywords: Hedonic Pricing Model; Property Attributes; Valuation Inaccuracy; Property 54Valuation; Lagos 55 56 57 58 596061===== PAGE 10 (score 30, numeric-paren 0, table-words 7, var-words 13) =====6263Journal of African Real Estate Research Volume 3(1) 2018 122-140 64 65 66131 67could result in a low crime rate), this would significantly increase the price of 68a property. However, this study found that the presence of a security fence (a 69security measure) in a property could negatively influence its price. This may 70be attributed to the fact that most of the residential neighbourhoods sampled 71usually have a general neighbourhood-organised security personnel in 72addition to each house’s security guards. Suggesting that the presence of a 73security fence in these neighbourhoods may not be regarded as a security 74measure for real estate investors. 75 76Table 4. Result of the Regression Analysis 77Independent variables 78Coefficient 79t-ratio 80p-value 81Bedrooms 828664822.596 830.738 840.461 85Bathrooms 86-20051336.870 87-1.448 880.149 89Property type 908076944.769 911.144 920.254 93Boys’ quarters 94102816020.589 9515.151 960.000 97Parking 989526774.351 992.615 1000.009 101Age 102-1635743.326 103-1.068 1040.286 105Floors 1064539570.089 1071.455 1080.147 109Security fence 110-10789691.840 111-0.229 1120.819 113Sea view 114154767562.522 1154.832 1160.000 117Location 118-26885403.503 119-6.176 1200.000 121Constant 12298778305.063 1231.396 1240.164 125r2= 0.766, Adj r2= 0.757, F-ratio= 80.381, n=256 126 127Of all the ten variables, the presence of a sea view and the number of boys’ 128quarters1 (BQ) in a property are the most highly significant variables that 129influence the price of a property. Both variables had the highest coefficients 130that contribute to the price formation of a property in the study area. In a 131similar study by Mok et al. (1995), it was found that a property that has a sea 132view will command a higher value. Also, the positive influence of the BQ 133room on the price of properties was reported in the study of Basu and 134Thibodeau (1998). 135 136Model Performance 137 138As earlier stated, in order to achieve the objectives of this study, the 139performance of model developed was tested using the MAPE, MAE and 140RMSE accuracy metrics. These approaches are commonly adopted in the 141literature (McCluskey et al., 2013), and the closer the value of both MAE and 142RMSE are to 0, the better the model (Lin & Mohan, 2011). MAPE is a 143measure of the prediction error expressed in percentage. The results shown in 144Table 5 reveals an MAE value of N61,408,856 and an RMSE value of 145N103,370.573. In addition, a MAPE value of 38.23% was recorded which 146depicts that the predictions produced an average error of 38%, which may not 147be acceptable by a rational real estate stakeholder. These suggest that there is 148 1491 Boys Quarters (BQ) refers to servants’ or maid’s quarters on a property 150151152===== PAGE 11 (score 13, numeric-paren 0, table-words 6, var-words 0) =====153154Journal of African Real Estate Research Volume 3(1) 2018 122-140 155 156 157132 158some level of inaccuracies in the HPM approach prediction, this is despite the 159satisfactory r2 value of 0.77. This confirms that the performance of a model 160could not be evaluated entirely by adopting the r2 value (Willmott, 1981). The 161findings of the study corroborate the position of Ogunba and Ajayi (1998) 162and Babawale and Ajayi (2011), who reported that the level of property 163valuation inaccuracy prevalent in the Nigerian appraisal practice is 164unacceptable based on the international standard. 165 166Table 5. Predictive Accuracy of the HPM Technique 167Accuracy metric 168Hedonic Pricing Model 169r2 1700.77 171MAPE (%) 17238.23 173MAE 17461,408,856.10 175RMSE 176103,370.573.40 177 178The HPM generated from the analysis was used to value the holdout sample 179in order to establish the predictive accuracy of the HPM technique. The result 180of the predictions was categorised to establish the percentage of the samples 181that fell within the acceptable margin and those that did not. The information 182in Table 6 shows that only 26.67% of the predicted property values have an 183error margin of between ± 0 and 10%, while 13.33% of the predicted values 184have an error margin of between ± 11 and 19%. In addition, 60% produced 185an error of ± 20%. It is worth mentioning that the inaccuracies recorded are 186high, resulting in wide disparities between the actual property values and 187predicted values (see Figure 2). 188 189Table 6. Valuation Accuracy of the HPM Prediction 190Accuracy range 191Frequency (n) 192Percentage (%) 193± 0-10% 1948 19526.67 196± 11-19% 1974 19813.33 199Above ± 20% 20018 20160.00 202 203 204 205Figure 2: Actual and Predicted Property Values 2060207500000002081000000002091500000002102000000002112500000002123000000002133500000002144000000002154500000002161217321852197220922111222132231522417225192262122723228252292723029231Actual Property values232HPM predicted values233Property prices234(N)235