A prediction model for housing investment probability

Abstract

This study investigates the government-conducted "Housing Demand Survey" from 2006Q4 to 2007Q3 to establish a binary logit model to analyze the housing attributes, search behaviors, and investors and owner-occupiers` personal characteristics for financial institutions prediction in determining house buyer motivation and reducing the loss incurred when payment is not advanced or is defaulted. The empirical results show that the locations, prices, sizes, and types of houses are the dominant factors affecting investment probability. Investors tend to select houses located downtown with high unit prices and small in size. They also tend to buy pre-sale, resold, and auction houses. The individual attributes of buyers, such as gender, age, occupation, and income, also play an important role in investment decisions. The proposed model has the highest prediction accuracy when the probability cutoff point is 0.7 and the investors` hit rate is 65.52% in contrast to the owneroccupiers` 84.51%, as indicated by the out-sample test. This study also determines that financial institutions incur the least loss with a cutoff point of 0.7 under different weights, when payment is not advanced or is defaulted.