Upgrading programme in public housing: an assessment of price and liquidity enhancements

Abstract

Upgrading programmes that aim to improve living conditions and enhance property values are often evaluated qualitatively. We propose a methodology for determining the market impacts of such projects and apply it for assessing the Main Upgrading Programme (MUP) in Singapore. This is a systematic S$15 billion project undertaken since 1992 to improve the condition of older public housing units. Both price and liquidity effects are examined. We find evidence that the programme has achieved its objective of value enhancement. Units that have completed upgrading fetch a significantly higher price while units that have been selected and successfully polled for upgrading fetch a small price premium. However, units undergoing upgrading sell at a statistically insignificant discount, ostensibly due to the disutility associated with construction. The MUP has little impact on the saleability of upgraded flats. The trade‐off in liquidity is seen only for units that have completed MUP. These take a longer time to sell.

Keywords:

• upgrading
• public housing
• price
• liquidity

Acknowledgements

The authors wish to thank Jack Chua for providing the data for this study and Yen‐Ling Koh for her research assistance.

Notes

Currently, the minimum period of ownership is 5 years.

We opt for price instead of valuation validation since the transaction prices are actually observed in the open market.

Most of the older flats are in good locations, close to the business district. To account for locational effects, we introduce new town/estate dummy variables.

See Teh (1975) and Teo and Savage (1985) for further documentation of public housing in the earlier years.

See Teo and Kong (1997) for more details.

HDB flats are built following the concept of new towns. New town planning is based on the idea of providing a town that is independent from the city and other major centres (Tan, 1994). In the context of high‐rise and high‐density housing in Singapore, this is done by providing facilities that will support the community (Wong and Yeh, 1985). New towns offer a fair degree of self‐sufficiency in terms of family/household, communal, recreational and educational needs, as well as providing work opportunities (Tan, 1994).

A 3‐room flat has only 2 bedrooms, while a 4‐room flat has 3 bedrooms, and so on. An executive flat has four bedrooms and separate living and dining rooms. One‐ and two‐room and three‐room flats form the balance not captured by room type dummy variables.

GEYLANG, KW and MP are in the default category not captured by estate dummy variables.

These papers provide excellent expositions on the technical issues associated with hazard models as well as sample selection bias, etc.

Another difference is that a log‐linear price model is used for this study rather than a linear price model in Ong and Koh (2000) and Ong et al. (2003). The log linear specification provided a better fit and less skewness in the residuals.

In a survey of more than 800 respondents, 42.6% cited inconvenience as a reason for not supporting the MUP.

These results are not reported but are available upon request.

These results are not reported but are available upon request.

*Statistically significant at 95% confidence level

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Dependent Variable = Log(Selling Price)

Reported t‐statistics are based on heteroskedasticity robust standard errors.

*Statistically significant at 95% confidence level

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Dependent Variable = Hazard Rate for TOM (Weibull distribution)

All other variables as specified in equation (6); PE = excess price as computed in equation (4); p = duration dependence parameter.