Abstract:
House rent prediction has great importance in real estate development as wel! as ill
overall housing situation of a city, The various participants in the real eslate market
have a substantial interest in the prediction of house rent. Rent models can be an
effective tool when empirical data carum! be collected either because of practical
constraints of cost, time etc. or when future scenarios are being dealt with. Hedonic
price (multiple regression) models have been commonly used to estimate house rent.
To address the issue of application of Artificial Neural Network (ANN) in house nonl
prediction, thIS study aims to develop an artificial neural network model for house
rent prediction. The study will also use the results from a hedonic price model for
house rent prediction and compare the predictive power of both models.
The data setu.sed to develop the Neural Network Model cOllsists of a sample of 479
single family and multi-family residential properties available for rent in Rajshahi
City. The neural network model built for this data set utilized fourteen independent
variables. The neural network models developed in this study arc the "beSI" models
that were obtained utilizing a sequential trial and error method. The best model
developed with eighty hidden neurons had the Rl valu.e of 0.621 for sample forecast.
The study has demonstrated that neighborhood attributes arc the most significant
factors in detemlining the house rent of Rajshahi City. The percentage of area
dedicated to community facilities and percentage of area dedicated to eommereial
use have contributed more to the predictive power of model than the other attribntes.
So it is seen that land usc has a great impact on honse rent in Rajshahi City.
The study also empirically compares the predictive power of the artificial neural
network lllodel with the hedonic price model on house rent prediction. The
eomp!,rison was conducted in six stages or cases. The results indicate that the neural
network model ou.tperformed the hedonic price model in all of the cases. In th,S
study, the ANN model consistently gave better result than the hedonic price model,
although the difference between the two models was not too large. ANN model and
hedonic price model both do better when they are trained and tested with the same
data set but they perfonned poorer on out-of -sample forecast. But in both cases
ANN model showed better results in comparison to hedonic price modeL The study
also supports the superiority of ANN model in prediction of outlier holdout sample.
