Hedonic Pricing Model With Respect to Gated Housing Attributes in Hyderabad

 

Akshay Bhanage¹, Abhimanyu Brahmankar¹, Ganesh Bagal¹, Prof. B. Ravinder²

¹ACM XXVIII Batch Students, NICMAR, Hyderabad

⁴Guide, Asst. Professor, NICMAR, Hyderabad

 

ABSTRACT:

The fostering IT sector in Hyderabad has developed a demand for new residential accommodation in the city of Hyderabad. The suburban and urban areas of the city have been witnessing a rise in number of new housing projects. This has lead to the change in the dynamics of the valuation of the housing units in the city. In this study, an attempt has been made to value the housing units based on its intrinsic and extrinsic characteristics. To do so, the Hedonic Pricing concept has been used which is based on the multiple regression techniques to regress the pricing function and to study the individual as well as the overall effect of the characteristics on the value of the property. Factors that determine the house prices in Hyderabad are analyzed in this study using data collected from various sources such as the property expo, site sales office, sample flat visit and company’s website. Data collected for 20 gated housing units in different areas of Hyderabad has been used to generate the model.  The results showed that the following variables were significant: number of stories, proximity to bus stand and railway station, number of rooms and whether the house is ready to move or not. They accounted for 88.44% of the variations. The Hedonic Pricing concept is thus used to model the relationship between the sale price of a house and the housing attributes which will prove as a basis for any real estate transaction for a project developer, a house buyer, loan granting banks and insurers.

 

 

INTRODUCTION:

The fostering IT sector in Hyderabad has developed a demand for new residential accommodation in the city of Hyderabad. The suburban and urban areas of the city have been witnessing a rise in number of new housing projects. This has lead to the change in the dynamics of the valuation of the housing units in the city. In this study, an attempt has been made to value the housing units based on its intrinsic and extrinsic characteristics. To do so, the Hedonic Pricing concept has been used which is based on the multiple regression techniques to regress the pricing function and to study the individual as well as the overall effect of the characteristics on the value of the property.

 

LITERATURE REVIEW:

The housing sector is very much associated with the economic health and wealth of a particular city. A high demand for housing would trigger growth in many other economic or commercial sectors of the city. Thus, studying the variables that impact property prices is essential because the purchase of a residential property is both an investment decision as well as a consumption decision. In the process to model housing prices, the hedonic pricing concept has been widely used.  Unlike other consumption goods, the housing market is unique because it depicts the characteristics of durability and heterogeneity. Thus, to incorporate this differentiation effectively, the hedonic pricing concept has been used widely. The hedonic price model proposes that goods are typically sold as a package of inherent attributes (Rosen 1974)¹. Therefore, the price of one house relative to another will differ with the additional unit of the different attributes inherent in one house relative to another house. The relative price of a house is then the summation of all its marginal costs estimated through the regression analysis. The theory assumes that a commodity such as a house can be viewed as an aggregation of individual components or attributes. Consumers are assumed to purchase goods constituting bundles of attributes that maximize their utility functions. Hedonic price theory originates from proposal that goods are inputs in the activity of consumption, with an end product of a set of characteristics. Numerous studies have utilized this technique to examine the relationship between attribute preference and the price of properties (Musili Joseph 2008²; Marissa Palin 2011³; Sibel Salim 2008⁴). This is because the market price of a housing unit can be determined by the buyer's evaluations of the housing unit's bundle of inherent attributes, such as location, structural, or neighborhood attributes (Freeman 1979)¹. Typically, housing attributes are classified into location attributes, structural attributes, and neighborhood attributes. These attributes encompass both quantitative and qualitative attributes (Musili Joseph 2008 ; Sibel Selim 2008). The market prices of the property can therefore be expressed as a function of location, structural, and neighborhood variables. The hedonic price approach allows us to estimate the individual effects of each housing attribute on housing prices, holding all other factors constant. In the paper by Sibel Selim (2008), research has been done on the determinants that affect house prices and the factors that determine the house prices in Turkey are analyzed using 2004 Household Budget Survey Data for urban, rural and for whole country. That data contains socioeconomic status, household composition, expenditure and purchasing type which is obtained from 2004 Household Budget Survey. The size of the estimation sample (5741) enables them extensive modeling of the housing characteristics. 46 variables together have been considered with the descriptive statistics. The variables include location characteristic, type of house, age of the building, type of the building, saloon and living rooms floors, bathroom floors, heating system, number of rooms, size (square meters), and other structural characteristics. Researcher does not consider environmental data. Semi logarithmic function form has been used as it fits well. The author has employed ordinary least square method in estimating Hedonic model. The results of the hedonic model reveal that water system, pool, type of house, number of rooms, house size, location characteristic and type of the building are the most significant variables that affect the house prices. This study has limitation of environmental factors that affects price of houses. Because of the larger size of the samples that enables researcher to proceed with higher no. of housing characteristics for Hedonic model. The paper by Musili Joseph (2008) has a main objective to determine critical factors that determine the residential property values, to demonstrate to what extent HPM can incorporate both qualitative and quantitative heterogeneous attributes of residential property in valuation estimates. To arrive at the results, the  Primary data was collected by use of structured close-ended as well as open ended questionnaires and in-depth discussions with selected respondents, the period 2008-2010 was chosen because housing rates were stable in that period and difference in housing prices was not influenced by macroeconomic factors. 15 variables taken for the study, which includes fence, plinth area, bedroom, bathroom toilets, ensuites, balcony, type of ownership, construction quality, location, amenities, age, type of house, year of sale. Descriptive statistical analysis, correlation analysis and regression analysis are the statistical technique preferred for the study. The paper gives an important indication regarding the correlation between the various attributes considered. Multi co-linearity in  modeling occurs when independent variables are highly correlated. Multi co-linearity is bad because it can adversely affect the multiple regression analysis results and therefore should be checked before the hedonic model is formed. The main advantage of the hedonic pricing concept is that one only needs to have certain information, such as the property price and the composition of housing attributes. The marginal attribute prices are obtained by estimating the parameters of the hedonic price function. It is a straightforward approach because only the coefficients of the estimated hedonic regression are needed to indicate the preference structure. No information whatsoever about individual characteristics or personal information of either the house buyers or the suppliers is required. Thus the literature depicts that the hedonic pricing model is a very useful scientific tool. With sufficient data, this tool allows us to estimate the individual effects of different housing attributes on housing prices. Since housing researchers cannot conduct controlled experiments in the laboratory, the hedonic price model is the major scientific method by which we can observe the effects of one or more housing attributes on housing prices, with the other factors holding constant. This allows us to understand the behavior of players in the housing market and how the housing market operates. This is very important for developers, development consultants, investment consultants, and policy makers.

 

CASE STUDY:

The study aims at using the Hedonic Pricing Concept for assessing the value of housing units in Hyderabad. The study comprises of data gathered from 30 gated housing properties in various areas of Hyderabad which are then regressed using the multiple regression analysis. The data was collected from various sources such as the property expo, site sales office, sample flat visit and company’s website.

 

OBJECTIVES:

·        To identify the critical factors that determines the residential property values.

·        To demonstrate to what extent Hedonic Pricing Concept can incorporate both qualitative and quantitative heterogeneous attributes of residential property in valuation estimates.

·        The main objective is to demonstrate the application of Hedonic Price Concept for estimating the house prices in Hyderabad.

 

LIMITATION:

·        Consumers purchase goods embodying bundle of attributes that maximize their underlying utility function.

·        Buyers and sellers have perfect information concerning housing product and price.

·        Hedonic price model only works under the assumption of market equilibrium.

·        The study limited Hyderabad and Ranga Reddy districts only.

 

SCOPE OF THE STUDY:

The study focus is on hedonic pricing method. The scope includes gathering information from various builders, promoters and also from buyers which includes cost  per square ft. , variables like flat area, distance from bus stand, distance from railway station, distance between market/s, distance from medical facilities, distance from working places etc.. The scope is defined to include the factors affecting the residential housing estimates. The residential housing was selected because it is highly transacted and therefore data on sales and valuation could easily be obtained. The physical location of the study is in the city of Hyderabad, India which has an active housing market.

 

RESULTS:

The collected data was  fed in to the excel sheet, transformed in to their natural log values - both independent and dependent variables and then multivariate regression analysis has been carried out. The results are as follows:

 

Results for Regression with Significant Variables:

Table 1: Regression Statistics

Regression Statistics
Multiple R	0.937362294
R Square	0.878648071
Adjusted R Square	0.859231762
Standard Error	0.078256673
Observations	30

 

Table 2: Model Summary of Regression

	Coefficients	Standard Error	P-value
Intercept	4.125698344	0.497051661	1.19E-08
Proximity to bus stand	-0.284297908	0.092858736	0.005205
House size in square feet	0.833671165	0.159465273	2.07E-05
Proximity to railway station	-0.108190994	0.048618817	0.035315
Floor level	0.124032558	0.054508709	0.031711

 

 

The coefficients and intercept are obtained in the Table 2 after analyzing the variables from the 30 houses. It indicates for how much percent the house value will change for a unit percent change in each characteristic if all other characteristics held constant. Therefore regression coefficient is  used to indicate the value of each independent variable. For example, from the above Table 2, with increase in size of the house by 1% in square feet, the price of the house will increase by 0.833% . A coefficient of -0.2843 for proximity to bus stand indicates that with increase in distance between the house and bus stand by 1% will decrease the house price by 0.2843% and vice-versa. Similarly, with increase in distance between the house and the railway station by 1% will decrease the house price by 0.1082% and vice-versa.  Also, an increase in unit percent in the floor level will cause an increase of 0.1240% in its price. The Standard Error term  indicates the difference between the observed value and the predicted value of the depended variable.  The p-value indicates the significance of the variables. The alpha value used in this model 0.05 so variables with p-value lesser than 0.05 are significant. From Table 2, the p-values for the variables - proximity to bus stand, house size in square feet, proximity to railway station and floor level are 0.005205, 0.0000207, 0.035315 and 0.031711  respectively.

 

INFERENCE:

·        The variables - proximity to bus stand, house size in square feet, proximity to railway station and floor level  have a p-value lesser than 0.05 which makes them significant for the model and they affect the variation caused in the house sales prices and only these four variables will be considered in developing Hedonic Regression Model.

·        The  regression result shows an adjusted R² of 0.8786,i.e., the combined effect of the 4 significant independent variables accounts for 87.86% on the dependent variable. An adjusted R² of 0.8786 shows the moderate strength of the model which is quite satisfactory.

 

Hedonic Model:

House Sale Price = [ (4.1256) +( -0.2843*Proximity to bus stand) + (0.8336 *House area in square feet) + (-0.1082 *Proximity to railway station) + (0.1240*Floor level)]

·        As the proximity to the bus stand from the apartment increases by 1%, the house of the price increases by 0.2843% and vice-versa.

·        The model depicts that with every additional rise of 1% in the house area in square feet, its price will increase by 0.8336%  and vice-versa.

·        As the proximity to the railway station from the apartment increases by 1%, the house of the price increases by 0.1082% and vice-versa.

·        With increase in floor level by unit percent, the house price will increase by Rs. 0.1240 and vice-versa.

 

CONCLUSION:

1.      In this particular case study, even though number of variables were considered; the  variables Proximity to Bus Stand, House size in square feet, Proximity to Railway Station and Floor Level are found to be significant and the same is observed from the market. The model fairly depicts the trend in variation of prices of different houses located at different localities in Hyderabad.

2.      In Hyderabad, buyers preferred to stay away from crowded colonies but preferred the means of commute to be nearer.

3.      The developed model indicates the information, which is in practice.

 

REFERENCES:

1.       Sherwin Rosen , “Hedonic Prices and Implicit Markets : Product Differentiation in Pure Competition”, The Journal of Political Economy, Vol. 82,Issue 1 (Jan-Fab 1974), 34-55.

2.       Musili Joseph , “Real Estate Valuation Based on Hedonic Price Model : Case Study on Residential Housing in Nairobi”, University of Nairobi, REG. NO. B92/72407/2008.

3.       Marissa Palin , “Hedonic pricing of the Colorado Lagoon in Long Beach, California”, Centre for Resource Management & Environmental Studies (CERMES) University of the West Indies (UWI), Cave Hill Campus September 16, 2011.

4.       Sibel Salim, “Determinants of House Prices in Turkey : A Hedonic Reression Model”, Doğuş Üniversitesi Dergisi, 9 (1) 2008, 65-76.

 

Received on 04.11.2015            Accepted on 13.12.2015            © EnggResearch.net All Right Reserved Int. J. Tech. 5(2): July-Dec., 2015; Page 153-156 DOI: 10.5958/2231-3915.2015.00012.7