A Review of Literature on Application of Hedonic Price Model on Residential Property Value in Nigeria

Abstract

The real estate market has long been influenced by a variety of factors, including location, amenities, and physical characteristics making property valuation a complex and multifaceted process. The Hedonic Pricing Model (HPM) has emerged as a powerful tool in assessing property values by considering the different attributes that contribute to market prices. This study examines previous literatures on application of HPM in the assessment of property value; by examining numerous studies, this study examined the theoretical aspects of the HPM specifications, explored potential commonalities in their application in different countries and providing insights into the model's versatility and adaptability in a range of market scenarios. The purpose of the study is to harmonize the current literature on field of research, hence helping more understanding of Hedonic Pricing Model (HPM) as an appraisal model. This is because the concept of Hedonic Pricing Model (HPM) has attracted much attention in recent years. The study is conducted with reference to existing theoretical literature, published and unpublished research. The study is mainly a literature review/survey on the implementation of HPM in the assessment of property value. The literature review found that value of residential property is positively correlated with structural attributes including lot dimensions, living space, quality of the external and interior construction, number of spaces, bathrooms, and bedrooms. Based on studies reviewed, Hedonic Price Model proves to be adaptable across diverse real estate markets worldwide, but its effectiveness varies depending on local market conditions, which means its application needs to be tailored to specific regional contexts. Given the versatility of the Hedonic Pricing Model (HPM) in various market scenarios, it is recommended that future studies explore the integration of newer data sources, such as big data and machine learning techniques, to enhance the accuracy and predictive power of HPM. Incorporating these technologies can help capture more nuanced and dynamic property value determinants, improving the model's application in real-time market conditions.