Lennon H.T. Choy

Quantile Regression Estimates of Hong Kong Real Estate Prices

Linear regression is a statistical tool used to model the relation between a set of housing characteristics and real estate prices. It estimates the mean value of the response variable, given levels of the predictor variables. The quantile regression approach complements the least squares by identifying how differently real estate prices respond to a change in one unit of housing characteristic at different quantiles, rather than estimating the constant regression coefficient representing the change in the response variable produced by a one-unit change in the predictor variable associated with that coefficient. It estimates the implicit price for each characteristic across the distribution of prices and allows buyers of higher-priced properties to behave differently from buyers of lower-priced properties, even if they are within one single housing estate. Thus, it provides a better explanation of the real-world phenomenon and offers a more comprehensive picture of the relationship between housing characteristics and prices.

Modelling planning control decisions: a logistic regression analysis on office development applications in urban Kowloon, Hong Kong

Abstract Planning control decisions are often characterized as results of a complex deliberation process that involves consideration of many factors. This exploratory study uses logistic regression to investigate whether it is possible, based upon a small number of quantifiable variables, to correctly predict the past decisions on 162 planning applications for commercial-office development in urban Kowloon of Hong Kong. Our final quantitative model identifies four key factors that can explain up to 77% of the decisions, but none of these factors are included in the formal planning guidelines. This study suggests the need for further research into the local planning control decision making process, particularly in relation to the issues of certainty and flexibility, the relationship between planning and market, and interaction between policy and decision.

Region-specific Estimates of the Determinants of Real Estate Investment in China

This paper utilises a reduced-form equilibrium model to investigate the possible sources of real estate investment differentials among 22 provinces, five autonomous regions and four municipalities of the People’s Republic of China. The model is estimated using panel data from 2001 to 2006, yielding a total of 186 observations. Specifically, empirical results suggest that demographic, economic and planning factors are the major determinants to cause real estate investment to vary among Chinese regions. The relatively small coefficient estimate of real interest rates indicates that it has a significant but modest impact. Based on the coefficient estimates, this paper finally suggests that the Chinese government should focus and work on several policy parameters in order to achieve a more balanced state of real estate investment across Chinese regions.

Forecasting the demand for construction skills in Hong Kong

Efficient manpower planning has been recognized as a critical aspect for the development of an economy. In 2001, the Works Bureau of the Hong Kong SAR Government (predecessor of Environment, Transport and Works Bureau) commissioned an HKPolyU consultancy team to develop a computer‐based model to estimate the demand for different categories of construction personnel. This article presents the concept and features of the manpower demand‐forecasting model developed for the construction industry of Hong Kong. The forecasting model is formulated on the basis of the labour multiplier approach by deriving the relationship between the number of workers required and the project expenditure in the given project duration. Multipliers for 61 project types were derived for 38 labour trades using completed project data. The labour demand by occupation for each project can then be estimated by multiplying the corresponding multipliers and the estimated project expenditure. Several unique features of the model have been developed, including “normalization” and “contract cost adjustment factor”. Normalizing the labour multipliers can facilitate the prediction of occupational labour requirements at different stages of a construction project. The adjustment factor is introduced to eliminate the discrepancy between the original estimates and final contract values so as to enhance the estimation accuracy. The model can also be used to predict the number of jobs created for a given level of investment. The government can apply this model to check and compare which project types will generate most jobs before committing public money. This model could be easily adopted and adapted by foreign construction authorities while planning manpower.

Housing attributes and Hong Kong real estate prices: a quantile regression analysis

By nature, people’s tastes and preferences are unique and diverse so that a constant coefficient of each housing attribute produced by ordinary least squares (OLS) is not able to fully describe the behaviour of homebuyers of different classes. To complement the least squares, quantile regression is used to identify how real estate prices respond differently to a change in one unit of housing attribute at different quantiles. Theoretically, quantile regression can be utilized to estimate the implicit price for each housing attribute across the distribution of real estate prices, allowing specific percentiles of prices to be more influenced by certain housing attributes when compared to other percentiles. Empirical results demonstrate that most housing attributes, such as apartment size, age and floor level, command different prices at different quantiles. With the use of this approach, the efficiency of the mortgage markets is enhanced by offering more accurate prediction of real estate prices at the lower and upper price distribution.

Region-Specific Estimates of the Determinants of Residential Investment

This paper adopts a reduced-form equilibrium model to investigate the possible sources of residential investment differentials among 22 provinces, five autonomous regions, and four municipalities in the People’s Republic of China. The model is estimated using panel data from 31 Chinese regions during the period between 2001 and 2006, yielding a total of 186 observations. Empirical results should hopefully shed light on the adjustment mechanism of the Chinese residential-property market. Specifically, this paper suggests that change in population, income, and business confidence are the major determinants that cause residential investment to vary among different regions. The relatively small coefficients of real interest rates and planning regulations suggest that they have significant but modest impacts on residential investment.

Let the Buyer or Seller Beware: Measuring Lemons in the Housing Market Under Different Doctrines of Law Governing Transactions and Information

Under information asymmetry, lemons tend to be overpriced. Yet how much of an overpricing premium the lemons can command is contingent on the underlying legal institutions. A set of transaction data from Hong Kong’s housing market reveals that durable lemons are overpriced by 6.7 and 9.9 percent under the rules of “let the seller beware” (caveat venditor) and “let the buyer beware” (caveat emptor), respectively. Switching the legal regime from the former to the latter produces a 32.3 percent increase in the overpricing premium. However, this does not suggest that caveat venditor is necessarily a more efficient legal doctrine. New information disclosure institutions are emerging to deal with the lemons problem.