A model to Estimate the Implicit Values of Housing Attributes by Applying the Hedonic Pricing Method

Abstract

Many scholars focused on the location based attributes rather than the non-location factors in decision making on land prices. Further, new research studies have identified the importance of the non-location attributes with the location factors. Many studies suggest that, many attributes exist which affects the housing price. Since the attributes involved and dominant for a particular case differs from one situation to the other, there cannot be an exact list of attributes. Yet, identification of factors that determine housing price and their relationships and the level of influence have poorly understood in planning and property development in the context of Sri Lanka. This study attempts to address what make householders to decide on housing price and application of hedonic pricing approach to estimate the implicit price of housing attributes in context of Sri Lanka. A sample study of selected fifty (50) single house transactions in Maharagama urban neighborhood area has been utilized to illustrate the applicability of the hedonic pricing model. As a methodology, correlation analysis has been carried out to study the degree of relationship between the housing price and the independent variables. The attributes which correlate with housing prices, the study identified the most significant attributes. A model was developed to estimate the future house price by applying the pricing model which is incorporated with these attributes. A hedonic house price model derived from multiple liner regression analysis was developed for the purpose. The findings reveal that six attributes as design type of the house, distance to the local road, quality of Infrastructure, garden size, number of the bed rooms and property age are contributed to estimate the implicit value of Housing property. The model developed would be used to identify implicit values of houses located in urban neighborhood area of Sri Lanka.

Hedonic Pricing Model for Real Property Valuation via GIS - A Review

“…Most commonly used building characteristics The three main categories of the building variables commonly used in the hedonic model are: (1) Structural characteristics, (2) Neighbourhood characteristics, and (3) Locational characteristics [1], [2], [8], [10], [13], [16], [17], [18], [19]. However, some studies merely concentrate on certain building parameters such as the effects of green spaces in contributing the property prices [10,16], whereas Cellmer [3] put interests only on the effect of noise intensity.…”

“…People were willing to pay for structural attributes rather than for the other two major attributes [19]. Older buildings show low price except that of historical value [2], [12].…”

“…The density population and purchasing power are positively related to the house price [8]. Green space amenity variables are positively significant [10], [16], [19]. Locations across from, or adjacent to, open space improve price, as do locations on cul-de-sacs or lakes [2].…”

“…Location of property in the areas of upgrading programs improves their price [12] as well as the existing of the property within a historic district [2]. However, the neighborhood attributes, traditional views, norms and customs were completely not significant in the work of [19]. Lastly, the locational characteristics in general produces negative effects.…”

An Assessment of Stumpage Price and the Price Index of Chinese Fir Timber Forests in Southern China Using a Hedonic Price Model

“…The hedonic price method (HPM) has been widely used in studies of commodity prices [21][22][23][24][25][26][27][28][29][30][31][32], and its use is particularly appropriate in contexts that entail heterogeneous production inputs whose underlying production characteristics demonstrate significant variations. Given differences in forest characteristics, including component tree species and their quality, size, or volume, forest assets are evidently heterogeneous commodities.…”