House Rent Estimation in Dhaka City by Multi Layer Perceptions Neural Network

Abstract

This paper attempts to build an artificial neural network that can estimate the median price of a home in a neighborhood described by forty demographic attributes in areas of Dhaka City namely Dhanmondi, Baridhara, Gulshan, Mirpur, Uttara and Old Dhaka. A structured questionnaire was used to collect the relevant data and the housing data sets was used to develop constant quality price indices using traditional econometric techniques and using neural networks incorporating genetic algorithms. Factors including house size, house age, house type, number of bedrooms, number of bathrooms, number of garages, amenities around the house and geographical location are considered. The analysis indicates that neural networks act as real alternative to the econometric methods. In this paper thirteen conditional attributes have been considered to estimate the house rent. This paper provides some indicative policy guidelines to handle the house rent problem in the Dhaka City and suggests that a rent controller should be appointed for each ward and maximum rents for particular areas should be gazette and the rents should be paid through banks.