DEVELOPMENT OF AN AUTOMATED RESIDENTIAL PROPERTY PRICE PRIDICTING SYSTEM USING LINEAR REGRESSION MODEL

Abstract

One of the most important investments in the world today is real estate, particularly in places like Lagos, Abuja, Rivers, and the like, which are also popular places for people to live and work. Therefore, before you spend your hard-earned money on any property, it is crucial to know the current valuation of the residence. This paper's primary goal is to forecast the present market value of residential real estate in Nigeria. While doing so, consideration is given to several factors, such as the number of bedrooms and the availability of various amenities. The purpose of this forecast is to assist a customer in finding feasible solutions that better fit their needs. The cost of the different residences in question was predicted using the linear regression model. This concept also benefits the customer by doing away with the necessity to speak with a broker. The purpose of the study is to determine how the dependent variable—asking price—relates to the exploratory factors, which include neighbourhood, garage capacity, number of bedrooms and bathrooms, land parcel size, and home square footage. The developed model will allow for the prediction of housing market values based on a variety of property attributes using the linear regression method. This would allow both buyers and sellers to make an initial appraisal of any property in Nigeria. The data for this study was gathered from an online resource known as the UCL Repository. The data collected for this study will be used to specify the model, generate parameter estimates, check for model inadequacy, repeat until the model is adequate, validate the model, repeat until the model is valid, and then use the final model to estimate asking prices for properties in each Nigerian neighbourhood. The property asking price is the dependent variable, and five quantitative independent variables using linear regression: home square footage, lot square footage, number of bedrooms, number of bathrooms, year of construction, and number of cars accommodated in a garage will be the main focus of this work.