House Price Index Construction in the Nascent Housing Market: The Case of China

Abstract

Most existing house price index construction methods are developed mainly based on transaction data from the secondary housing market, and are not necessarily suitable for the nascent housing markets where a predominant portion of housing transactions are new units. Using the booming market in China as an example, we evaluate and compare the performances of three most common house price measurement methods in the newly-built housing sector, including the simple average method without quality adjustment, the matching approach with the repeat sales modeling framework, and the hedonic modeling approach. Our analyses suggest that the simple average method fails to account for the substantial complex-level quality changes over time of sales during our sample period, and the matching model fails to control for the effect of developers’ pricing behaviors when adopted in the newly-built sector, hence both are downward biased. Based on this finding, we apply a hedonic method, which allows us to control for both quality changes over time of sales and developers’ pricing behaviors, to 35 major newly-built housing markets and provide the first multi-city constant-quality house price index in China. The new index reveals that the current Chinese housing market is facing a greater risk of mispricing than reported by the existing official metrics.

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Notes

  1. 1.

    Among others, see Hui and Shen (2006), Yang and Shen (2008), Chen et al. (2010), Ahuja et al. (2010), Dreger and Zhang (2010), and Wu et al. (2012) for example. There are also a large number of related studies published in Chinese.

  2. 2.

    The “Average Price Index” covers all Chinese cities since mid-1990s. It is the obligation by law for all developers in China to regularly report stated business indicators to the government statistics agency, including the total volume (in floor area) of newly-built housing units sold within this period and the total price of these units. By aggregating these reported figures at various levels, and dividing the total price by total floor area of the transacted units, the average house prices are calculated and reported at the city, province, and national level, respectively.

  3. 3.

    The “70 Cities Index” originally covered 35 major cities since 1997 and then expanded to 70 cities in 2005. In each month technicians from local statistics authorities are sent to sample housing complexes to collect raw information on house transaction prices. The matching approach is then adopted to calculate the index, which will be discussed in detail later in “The Matching Approach with the Repeat Sales Modeling Framework”.

  4. 4.

    As a more well-known example, while the government and the general public in China were highly concerned with the apparent surge in house prices in 2009, the “70 Cities Index” suggested that nominal house price at the national level only increased by 1.5 % in 2009 compared with the previous year, which immediately generated great suspicions and criticisms. See the reports from Financial Times (“Fears of China Property Bubble Grow”, Mar 10, 2010) or China Daily (“Doubts over Increase in Property Price”, Feb 27, 2010) for more details.

  5. 5.

    See the reports from Wall Street Journal (“China Scraps High-profile Property Data”, Feb 17, 2011) for more details.

  6. 6.

    As another unique factor, currently the reported transaction prices of resale units are not considered of high quality in China, at least partially because an unknown number of buyers/sellers are reporting lower values to avoid transaction taxes and capital gain taxes. By contrast, house price data in the first hand sales are much more reliable, since developers are facing stricter audits.

  7. 7.

    Among others, see Wang (2001, 2003), Wu et al. (2007) for more details about Chinese housing reform.

  8. 8.

    According to the statistics published by National Bureau of Statistics of China in the “Statistics Yearbook of China”, the percentage of condominium units in the newly-built housing market kept around 94–96 % during the past decade in the national level. For example, in 2010 condominium units accounted for 95.5 % in floor area of all newly-built housing units sold.

  9. 9.

    Source: MHURD, “China Urban Construction Statistical Yearbook”. Besides, see Wu et al. (2007), Deng et al. (2010) for example for a more detailed review on housing suburbanization and urban expansion in China.

  10. 10.

    Source: Authors’ calculation based on the “Statistics Yearbook of China” published by the NBSC.

  11. 11.

    Note that presale of newly-built housing units is permitted and very popular in China, which means that developers can get the permit and sell housing units to households before the units are completed and ready to deliver. Both the developer and the buyer of a presale unit are contractually bound to complete the transaction upon completion of the unit.

  12. 12.

    For instance, according to the statistics provided by the MOHURD, in 2010 the average time-on-market (TOM) of all newly-built housing transactions in 35 major cities was about 9.2 months.

  13. 13.

    The determinants of listing prices and their effects on expected TOM in the resale housing markets have been explored in many existing researches (Haurin 1988; Forgey et al. 1996; Anglin et al. 2003), although so far there are still no similar researches in the newly-built sector.

  14. 14.

    In the Chinese newly-built housing markets, typically the developers’ marketing cost include rental and maintenance cost of the display space, wages and bonuses for the sales staff, advertising cost, etc. Most of these costs are duration costs, which means that they would happen at almost the same rate every day no matter whether any transactions are achieved. Besides, a large portion of the costs are fixed and could not be easily adjusted according to the number of units left (e.g., the cost of the display space, which contributes for a major portion of the marketing cost).

  15. 15.

    Among others, see Northcraft and Neale (1987), Genesove and Mayer (2001), Leung and Tsang (2011) for example.

  16. 16.

    Similar phenomenon also exists in the recession period. It is difficult for developers to lower the listing price of unsold units’ even if the market conditions turn down, because the households that already purchased units in the same complex always strongly oppose, or even require to refund.

  17. 17.

    Among others, see Case et al. (1991), Haurin and Hendershott (1991), Case and Szymanoski (1995), Deng and Quigley (2008), Nagaraja et al. (2010) for a literature review on the application and potential problems of the repeat sales method.

  18. 18.

    Theoretically, besides the repeat sales method, we could also apply the hybrid method developed by Case and Quigley (1991), Quigley (1995), Hill et al. (1997) to the matched pairs, where information from both repeat sales and single sales are both utilized. However, since only few (if any) transactions are unmatched in the matching approach, which means that only little information is dropped in the estimation of repeat sales method for these matched pairs, the hybrid method’s improvement compared with the repeat sales method could be expected to be very limited. Accordingly we do not discuss the hybrid method detailedly in this paper.

  19. 19.

    We are required by the data provider not to report the name of the city.

  20. 20.

    If more than one unit sold in previous periods shares the same distance in propensity score to the object unit, the unit sold last would be selected. See Deng et al. (2012) for more details about the matching procedures.

  21. 21.

    Because the effects of developers’ pricing strategy only exist in the newly-built housing markets, these results do not violate the conclusions in earlier researches (McMillen 2008; Deng et al. 2012) about the appropriateness of the matching approach in the resale sector. Actually our empirical tests based on resale housing transactions in the same sample city point out that, the unit-level matching approach could achieve in consistent results with the hedonic method, while it does not require any information on complex-level attributes. It also overbids the other methods judging from indicators of width of 95 % confidence interval and standard deviation of error term. Therefore we believe the matching approach with the repeat sales modeling framework is still a preferable choice in resale house price measurement in nascent markets like China. The results are available on request.

  22. 22.

    We had to define a standard specification for hedonic models in all these cities (which was almost the same as the specification discussed in Table 2; the only difference is the variables of TOPFLOOR and FIRSTFLOOR were not included), since we were now allowed to choose a most suitable specification for each city respectively in that condition. Besides, it was also difficult for us to adopt more complicated estimation methods than OLS in that simple program. We leave these for future studies.

  23. 23.

    The accumulated sample volume included in the index calculation in these 35 cities from 2006 to 2010 is about 8.3 million units.

  24. 24.

    One possible explanation is, at the end of 2005 the ratio between urban area and total area in Shenzhen already reached 0.32 (calculated based on the statistics published by MHURD), the highest in all these 35 major cities. This implies there is comparably less space left for further urban expansion in Shenzhen. Therefore the trend of housing suburbanization is weaker in Shenzhen during the sample period.

  25. 25.

    According to the statistics published by the NBSC, between 2006 and 2010 the real annual growth rate for GDP, per capita GDP and per capita disposable income in urban area in national level was 10.8 %, 10.5 % and 9.5 %, respectively.

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Acknowledgments

We thank the editors and the anonymous referee, Zhi Dong, John Quigley, Joseph Gyourko, and participants in the 2011 Asia-Pacific Real Estate Research Symposium and the 2011 AsRES & AREUEA Joint Conference for helpful comments. Liu and Wu thank the National Natural Science Foundation of China for financial support (No. 70873072 & 71003060). Deng and Wu thank the Institute of Real Estate Studies at National University of Singapore for financial support. Wu also thanks Peking University—Lincoln Institute Center for Urban Development and Land Policy for research support. We remain responsible for all remaining errors.

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Wu, J., Deng, Y. & Liu, H. House Price Index Construction in the Nascent Housing Market: The Case of China. J Real Estate Finan Econ 48, 522–545 (2014). https://doi.org/10.1007/s11146-013-9416-1

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Keywords

  • House price index
  • Hedonic method
  • Quality change
  • Nascent housing market

JEL Classification

  • C43
  • C81
  • R31