# RECORD 8885ddb1a206684210159fea8394d2ea9c611111 # TITLE: Natural Environmental Amenities and House Rentals: A Hedonic Analysis for Integrated Planning # YEAR: 2018.0 VENUE: Journal of African Real Estate Research DOI: 10.15641/jarer.v0i0.482 # AUTHORS: F. Famuyiwa # PAGES: 19 CANDIDATE TABLE PAGES (1-based): [11, 13] # ABSTRACT: Environmental valuation techniques have been used in quantifying the influences of environmental amenities on house prices. The hedonic approach is used to evaluate contributions of environmental amenities to rents in this study. Though the theme of this study has attracted pedagogical attention, the comparison between man-made and natural environmental amenities in housing values is a nouvelle approach in identifying and explaining effective demand and value systems of urban and natural amenities through market evidence. It extends existing body of literature on hedonic studies in Nigeria by contextually assessing these two broad categories of environmental amenities on the same scale. Results reveal the variations in demand and significance of physical infrastructure and natural amenities in rents. This /study's significance lies in bringing to light, the realities of market demand for natural amenities in comparison with physical infrastructure ===== PAGE 1 (score 7, numeric-paren 0, table-words 0, var-words 7) ===== 1 ffamuyiwa@unilag.edu.ng Journal of African Real Estate Research Volume 3, Issue 2 www.journals.uct.ac.za/index.php/JARER/index Natural Environmental Amenities and House Prices - A Hedonic Analysis for Integrated Planning Funlola Famuyiwa1 1 Department of Estate Management, Faculty of Environmental Sciences, University of Lagos, Nigeria To cite this article: Famuyiwa, F. (2018). Natural Environmental Amenities and House Prices - A Hedonic Analysis for Integrated Planning. Journal of African Real Estate Research, 3(2), pp.44-62. DOI: 10.15641/jarer.v0i0.482. Abstract Environmental valuation techniques have been used to quantify the influences of environmental amenities on house prices. Although this theme has attracted pedagogical attention internationally, a Nigerian perspective relating to effective demand and value systems of natural environmental amenities is lacking significantly. The gap in local market evidence relating to this topic is apparent in comparison to other real estate attributes such as physical infrastructure. In this study, the hedonic pricing model (HPM) is used to evaluate the contributions of environmental amenities to the rental values of detached houses in a sub- market in Lagos, Nigeria. This paper, therefore, extends existing body of literature on hedonic studies by contextually assessing the broad category of natural environmental amenities in Lagos. Using a sample of 111 properties, the HPM was used to reveal value inferences of various environmental characteristics on house rents. Results show that ‘green environment’ as a natural amenity in the housing market studied, produces the highest variation in rental values. This study’s significance lies in bringing to light the realities of market demand for natural amenities which, in turn, will help decision makers and public authorities in the strategic planning of the preservation and sustenance of natural amenities. Keywords: Environmental Valuation; Housing Market; Natural Amenities; Physical Infrastructure; Rental Values 1. Introduction There is a growing awareness of the importance of natural environmental amenities for public health, productivity, social and economic development, among others. For example, Littenberg et al. (2015) found that residing in areas with access to the natural environment is associated with better health, which increases the demand for specific housing attributes, such as proximity to green areas, water bodies, and other natural physical ===== PAGE 11 (score 17, numeric-paren 0, table-words 2, var-words 10) ===== Journal of African Real Estate Research Volume 3(2) 2018 54 Wind Quality in Building (Ventilation) WINDQUA Good = 1; Poor= 0 Landscape of Neighbourhood LSCAPE Good = 1; Poor= 0 View of Navigable water body VIEWWAT Good = 1; Poor= 0 4. Findings and Discussion In Table 2 the descriptive statistics of the sampled residential properties in the study area are displayed. Apart from ‘net rental value’, ‘living room’, ‘bedroom’, ‘bathroom’, and ‘plot size’, all other variables were estimated using binary measurements. The average net rental value (dependent variable) of detached buildings in the area is N1,604,879 while maximum and minimum values are N3,600,000 and N1,450,000 respectively. Table 2: Descriptive Statistics of Regression Variables MINIMUM MAXIMUM STD MEAN RENT 1,450,000 3600000 238030 1,604,879 STRQUAL .00 1.00 .24085 .92333 REPAIR .00 1.00 .32048 .81775 LIVIN 1.00 2.00 .04822 1.61098 BED 3.00 5.00 .28616 4.00723 BATH 2.00 5.00 .48250 3.59881 STORE .00 1.00 .49983 .555641 BCNY .00 1.00 .22081 .880378 PLOT 592.73 1,406.91 193.68204 736.75291 NTLIGHT .00 1.00 .00985 .68080 DISCBD .00 1.00 .39225 .61046 DISRECR .00 1.00 .40391 .42907 SECRTY .00 1.00 .36119 .64816 SERENE .00 1.00 .00839 .32832 ROAD .00 1.00 .40071 .63784 WATER .00 1.00 .23641 .60120 WASTE .00 1.00 .21093 .76059 SRTLIGHT .00 1.00 .38658 .32199 DRAIN .00 1.00 .47550 .66493 GREEN .00 1.00 .10993 .78036 GARDEN .00 1.00 .09001 .29732 WINDQUA .00 1.00 .33029 .62435 LSCAPE .00 1.00 .30723 .57446 VIEWWAT .00 1.00 .00033 .00778 Source: Field Study, 2016. ===== PAGE 12 (score 29, numeric-paren 1, table-words 10, var-words 7) ===== Journal of African Real Estate Research Volume 3(2) 2018 55 From Table 2, it can be seen that the number of bedrooms ranges from 3 to 5 with a standard deviation of 0.28616, implying a low dispersion around a mean of 4.00723. The mean for ‘natural lighting’ is 0.68080. Sixty-five per cent (65%) have ‘good neighbourhood security’, while the conditions of road surfaces of about 64% of these properties are good. Only about 60% of the properties have access to public pipe-borne water, and about 76% dispose of their domestic waste using government waste disposal services. Only about 32% of the properties have streetlights around their premises. Other variables can be interpreted from the mean values and standard deviations in the table accordingly. 4.1 Model Evaluation There is little guidance from economic theory about the proper functional relationship between house price and its attributes (Selim, 2008). The estimation and selection of appropriate HPM have therefore been of significant concern. Widely used functional forms include the ‘linear' form, the ‘semi-logarithmic' form (log-linear) and the ‘log-log'. Consequently, this study analysed field data using these three models. The three models utilised, depict the preponderance of these methods in similar studies. All three models used, (only the log-linear model shown) yielded significant coefficients and good model statistics. The log-linear model performed most satisfactorily, in terms of its ability to predict property value influences in the study area as indicated by its R² and Adjusted R² statistics of 0.790123 and 0.723652 respectively. An R2 of 0.790123 infers that about 79% of variance in rental values is accounted for by the joint influence of all explanatory variables. The adjusted R2 explains only the variation accounted for by the significant variables. The individual relationship of each of the attributes to value is shown in the second column of Table 3 (tagged ‘coefficients’). Babawale and Johnson (2012), remark that high values of R² do not necessarily ensure the accuracy of models. Hence, tests were carried out to check for ‘multicollinearity’ and ‘heteroskedasticity’. Multicollinearity is a statistical phenomenon when two or more independent variables in a multiple regression model are highly correlated (Xiao, 2017). This is undesirable in statistics as the estimate of one variable's impact on the dependent variable while controlling for the others tends to be less precise. ‘Heteroskedasticity’ on the other hand refers to a statistical phenomenon where the variance of the dependent variable varies across the data. Babawale and Johnson (2010) imply that the variance of the errors of the data increases or decreases. The White’s test and Variance Inflation Factor (VIF) statistics were used to check for heteroskedasticity and multicollinearity respectively. In the White’s Test, the F-statistic of 1.682110, the Obs*R-squared (51.481659), and p-values indicate constancy of the random error. The P-value of 0.043974 is the probability that random error is not heteroskedastic (not higher than 5 per cent). Table 5 shows a maximum VIF value of 2.99256, which is low, desirable and indicative of the absence of multicollinearity in the model. If the VIF of a variable exceeds 10, that variable is said to be highly collinear and will pose a problem in the analysis (Hair et al., 2006 cited in Hill & Throshani, 2009). All these imply that the data fits well into the regression line and the model is adequate in explaining changes in rental values for the category of ===== PAGE 13 (score 19, numeric-paren 0, table-words 6, var-words 4) ===== Journal of African Real Estate Research Volume 3(2) 2018 56 property in the study area. Consequently, the model appears adequate for predicting rental values of detached buildings in the study area. Table 3: Regression Results Log-linear Model Variables Coefficients T- statistics STRQUAL .191025 1.733654 REPAIR .081544 1.380998 LIVIN .084432 3.994811 BED .072237 3.410468 BATH .061126 4.736524 STORE .090283 3.028461 BCNY .152038 3.842652 PLOT .008826 3.947261 NTLIGHT .009435 6.884632 DISCBD .280001 1.920375 SECRTY .000983 1.677463 ROAD .120567 1.814028 WATER .097635 2.376251 WASTE -.039012 -3.429712 SRTLIGHT .050754 1.092836 DRAIN .009127 2.348283 GREEN .170362 2.736555 WINDQUA .011275 1.983463 LSCAPE .093552 5.012254 VIEWWAT .034900 4.773652 Constant 14.994821 37.839204 R2 .790123 Adjusted R2 .723652 F-statistic 19.336092 Prob .050000 Source: Field Study, 2016 Table 5: Collinearity Statistics VIF STRQUAL 1.73635 REPAIR 2.00483 LIVIN 1.04877 BED 1.94763 BATH 1.04987 STORE 1.77465 BCNY 2.53684 PLOT 2.99256 NTLIGHT 1.02736 Table 4: White’s Test F-Statistic 1.682110 Probability 0.043974 Obs*R-squared 51.481659 Probability 0.408321 ===== PAGE 14 (score 26, numeric-paren 0, table-words 7, var-words 11) ===== Journal of African Real Estate Research Volume 3(2) 2018 57 DISCBD 1.99715 SECRTY 1.37541 ROAD 2.55301 WATER 1.37599 WASTE 1.90548 SRTLIGHT 2.40918 DRAIN 1.28285 GREEN 2.48635 WINDQUA 2.34975 LSCAPE 1.11906 VIEWWAT 1.65421 4.2 Interpretation of Independent Variables The results from the log-linear model as seen in Table 3 indicate real estate attributes (variables) that predict rental values as described in Table 1. Variables with negative coefficients imply an inverse relationship with rental values and vice versa. The coefficients in the log-linear model are interpreted as variations in rental values when there is a change (per measurement unit) in a given variable. Accordingly, ‘good structural quality’ contributes 19% to rental values. For every additional living room, bedroom and bathroom, rental values increase by 8.4%, 7.2% and 6% respectively. Other independent variables in the table can be interpreted in the same way. For variables categorised as ‘natural environmental attributes’, the study suggests that ‘green environment in neighbourhood’ accounts for 17% of variation in rental values, while ‘good landscape’ contributes up to 9.3% to rental values. In terms of physical infrastructure; ‘government waste disposal services' possesses an adverse effect on rental values implying that houses serviced by government waste management agencies will likely experience a 3.9% decline in rental value, thereby contradicting existing theory of the positive effect which infrastructure has on property values. Further investigation into this suggests that this is because the erratic and unreliable state of government services has given rise to aesthetic issues with waste littering the streets due to delays in waste collection. This thereby constitutes a negative externality and impacts negatively on rental values. Although the distance to Central Business District (CBD) possesses the highest coefficient value (confirming location as one of the most significant factors to property value), its significance level is low. Natural environmental amenities performed almost at par with human-made amenities from the coefficient values. However, the strongest unique contribution (asides distance to CBD) explaining rental values is ‘green environment in neighbourhood' with the highest coefficient value of 0.170362 (17%). This implies that this particular variable is highly valued. Natural environmental amenities like ‘view of navigable water body', account for only about 3.4% of variation in rental values in the study area. Some independent variables which are excluded in the regression table are due to their insignificance, e.g. ‘distance to recreational centres (DISRECR)'. The variables ‘plot size (PLOT)' and ‘private garden space (GARDEN)' were found to highly correlate, thus introducing the problem of