Elsevier

Journal of Housing Economics

Volume 29, September 2015, Pages 41-58
Journal of Housing Economics

Home price beliefs: Evidence from Australia

https://doi.org/10.1016/j.jhe.2015.05.002Get rights and content

Highlights

  • We develop novel empirical estimates of home price beliefs.

  • We find homeowners slightly overestimate their own home prices on average.

  • There is a clear distribution of beliefs with evidence of optimism and pessimism.

  • Beliefs are associated with age, income, tenure and unemployment.

  • Optimistic neighbourhoods consume more, leverage more and choose riskier portfolios.

Abstract

New facts are documented about self-assessed home valuations using household panel data and a near-census of home sale prices. Between 2002 and 2012, homeowners’ display a small positive bias of around 1% in estimating the market value of their homes, although there is considerable dispersion in beliefs and prices. Household characteristics, including age, tenure, and income and local area characteristics, such as unemployment, are associated with differences between beliefs and prices. The extent of overvaluation is positively associated with household spending, leverage and risky-asset holdings. Over the housing cycle, homeowner valuations appear less volatile than sale prices and are backward-looking; homeowners also learn from past ‘errors’. These facts support recent literature on the importance of belief formation for household decision-making.

Introduction

Housing is the largest component of household wealth in Australia. Variation in housing prices has been shown to be important for household leverage, portfolio allocation decisions and consumption (Ellis et al., 2003, Kohler and Smith, 2005, Berger-Thomson et al., 2009, Windsor et al., 2013). However, timely data on the prices of individual homes are not readily available. For this reason, households are typically required to infer or form a belief about the value of their home when making these economic decisions. As the quote above from the Australian film The Castle illustrates, these beliefs can be quite subjective.

This paper explores homeowners’ beliefs about housing prices in Australia. Our goal is to provide insight into the differences between homeowners’ beliefs and market-inferred home sale prices, and whether these are important for economic decision-making. Our paper makes three contributions:

  • 1.

    We estimate the difference between beliefs and prices (hereafter ‘home valuation differences’) in a way that is free of recollection bias.

  • 2.

    We explore correlation between home valuation differences and various household characteristics (for example, age, income and education), the local area unemployment rate and a proxy for housing market information (the tenure of ownership).

  • 3.

    We investigate whether the size of home valuation differences across neighbourhoods is correlated with household spending, leverage and the share of risky assets held in households’ financial portfolios.

Understanding how well Australian homeowners assess the value of their homes is important for a number of reasons. First, self-assessed home values sourced from household surveys are the main source of data used to measure the distribution of household wealth (and related financial indicators, such as leverage). If homeowners do not accurately value their homes, then survey measures of household wealth may be biased. For example, if home valuation differences vary systematically with age then the estimated age profile of household wealth using self-assessed home values will be biased, giving a misleading picture of the actual distribution of wealth by age.

Second, by focusing on the distribution of average differences in beliefs and prices across neighbourhoods, our approach provides insight into alternative theories of homeowner belief formation. In particular, we consider whether beliefs are unbiased on average (rational) or whether there is skewness in beliefs that could reflect optimism or pessimism. Some models of decision-making under uncertainty that focus on factors such as robust control (Hansen and Sargent, 2008; Bidder and Smith, 2012) and ambiguity (Epstein and Schneider, 2008) predict that some households may hold pessimistic beliefs and therefore undervalue their homes.

In contrast, Genesove and Mayer (2001) show that loss aversion may cause some homeowners to hold optimistic beliefs relative to market-inferred prices when prices are declining. Likewise, the recent literature on optimism and other rational biases (see, for example, Van den Steen, 2004 and Brunnermeier and Parker, 2005) predicts that some households may hold optimistic beliefs and hence overvalue their homes. In particular, households may trade off the utility gains from optimism with any costs from making distorted decisions because of overvaluation. We provide empirical evidence that speaks to these alternative theories of belief formation.

A novel feature of our paper is the data we use. Our data include a census of all sales in Australia’s three largest cities, Sydney, Melbourne and Brisbane and cover around half of all sales in the Australian housing market. These data also cover several dwelling price cycles and a much longer time span than comparable best-practice studies (see, for example, Agarwal, 2007, Henriques, 2013). This permits more accurate inference about the determinants and effects of beliefs over the full dwelling price cycle, rather than being conditional on a single market upswing or downturn in prices.

We use hedonic regressions to measure the average price of homes in a homeowners’ locality (at a very disaggregated level) and match it to the timing of self-assessed home valuations. By using this alternative method, differences between market-inferred and self-reported values can be measured in a manner that is free of bias in the homeowner’s recalled purchase price, and that is free of any distortion that occurs from using a price index for a broad geographic region to estimate the market value of an individual home.

The early research instead compared estimates of housing prices by homeowners and professional appraisals.1 The literature has also compared self-assessed home values to recalled sale prices. For Australian homeowners, Melser (2013) assesses home valuation differences in this way and finds a positive bias of around 4%. In other studies, homeowners that have recently moved are surveyed and asked to make an assessment of the current value of their homes, as well as recall the original sale price of their homes. Local housing price indices are typically used to control for the passage of time between the current estimate and the initial purchase price.2

The limitation of these approaches are the small samples of recalled purchase prices (generally less than 1,000 observations); their inability to distinguish between valuation bias and recollection bias;3 and their reliance on external indices to update self-assessed home values.4

In contrast to previous literature, we find that homeowners’ home price beliefs exhibit only a small positive bias of around 1%. In terms of the absolute differences, we find that half of the average home valuations fall within 11% of the average market value across neighbourhoods. However, while beliefs are essentially unbiased on average, we do find statistically significant differences between average beliefs and average sale prices for many neighbourhoods. In particular, a relatively large share of neighbourhoods are undervalued (have a significant negative valuation difference) and a relatively large share of neighbourhoods are overvalued (have a significant positive valuation difference).

Certain average household characteristics are correlated with valuation differences. In particular, neighbourhoods with older homeowners and higher disposable income are more prone to overvalue their homes, on average. In contrast, regions with relatively high unemployment are more likely to undervalue their homes, on average, while regions in which homeowners have lived for a long time (have greater information) tend to have more accurate valuations.

We also explore how home valuation differences are associated with households’ consumption and financial decisions – that is, we examine whether beliefs matter. We find evidence that valuation differences are positively associated with spending, leverage and the allocation of wealth to ‘risky’ assets, such as equities, after controlling for a number of other factors, including average income and the average sale price of homes in the neighbourhood.

Importantly, we show that our results are unlikely to be due to omitted characteristics in the hedonic regression. Our key findings also hold under an alternative approach to estimating home valuation differences using repeat sales.

Section snippets

The formation of home price beliefs

The idea that households form subjective beliefs about the value of their own home is intuitive. Unlike financial assets such as equities, housing is an asset that is relatively hard and costly to value.5 The main reasons for this are:

  • 1.

    Housing is a heterogenous asset that is not sold on a large centralised market. Instead, there are non-trivial search costs for buyers and sellers to successfully match

Data

Our benchmark measure of the market value of the home is derived from unit-record data provided by Australian Property Monitors (APM).7 Our measure of self-reported home valuations is obtained from the Household Income and Labour Dynamics in Australia (HILDA) survey.

Details on the exact location of properties are available in the APM sales dataset, but are not available in the HILDA

Hedonic methodology

A simple metric for measuring home valuation differences would be to compare, at a given point in time, the average self-assessed home value of each surveyed homeowner within a neighbourhood (from the HILDA survey) to the average price of all homes sold in that neighbourhood (from the APM dataset). For example, using the available data for Sydney over the period 2002–2012, we would obtain 1683 (=11 years × 153 neighbourhoods) price comparisons; the mean of which would measure the overall home

Distribution of home valuation differences

Using home valuation differences to make inferences about household belief formation is the focus of this paper. As discussed in the previous section, we could construct these valuation differences by comparing the (unconditional) average sale price to the average self-assessed home value within each neighbourhood and time period. This comparison is given by Eq. (3):Upt=ln(Vpt)¯-ln(Spt)¯.However, this measure confounds a comparison of ‘true’ prices with changes in the composition of homes sold

The dynamics of housing price beliefs

We next examine the time-series properties of home price beliefs and how beliefs evolve over the housing cycle. In general, we find evidence consistent with (1) homeowner valuations being ‘sticky’ compared to sale prices, (2) homeowner valuations being backward-looking and (3) homeowners learning from past ‘errors’.

First, home price beliefs and market prices follow very similar cycles, on average (Fig. 6). However, city-level beliefs are not as volatile as market prices, suggesting that the

Panel regressions

We now examine whether home valuation differences are systematically related to household and regional characteristics. To do so, we estimate several panel regressions of the following form:Uptadj=α+Xptβ+θp+δt+pt,where the dependent variable is the estimate of the average valuation difference (Uptadj) in each neighbourhood and time period. A higher value for the dependent variable indicates greater overvaluation relative to the market sale price for a neighbourhood. The specification includes

Homeowners

We now examine whether home valuation differences are associated with the economic decisions made by homeowners. In other words, in a regression framework, we now treat the valuation differences across neighbourhoods as an independent variable. Specifically, we assess whether valuation differences are correlated with household spending, leverage and portfolio decisions. For instance, if optimistic homeowners typically overestimate the value of their homes we might expect that they spend more

Robustness tests

A condition for consistent estimation of the home valuation differences in our benchmark model is that any omitted housing characteristics in the hedonic models (for example, the number of bathrooms) should not have differential effects on market prices as compared with self-assessed values. To determine the extent to which our main results are robust to such omitted variables we construct the valuation differences using an alternative repeat-sales methodology. This is a useful alternative as

Conclusion

In contrast to the existing literature, this paper provides an approach to measuring home valuation differences that is free of recollection bias. We also study the determinants of valuation differences and whether valuation differences are important for economic decisions.

We find that homeowners’ housing price beliefs – measured at the neighbourhood level – are generally unbiased. However, there is significant variation around this unbiased mean: while around half of all neighbourhoods provide

APM disclaimer

The Australian property price data used in this publication are sourced from Australian Property Monitors Pty Limited ACN 061 438 006 of level 5, 1 Darling Island Road Pyrmont NSW 2009 (P: 1 800 817 616).

In providing these data, Australian Property Monitors relies upon information supplied by a number of external sources (including the governmental authorities referred to below). These data are supplied on the basis that while Australian Property Monitors believes all the information provided

HILDA

The following Disclaimer applies to data obtained from the HILDA survey and reported in this article.

References (29)

  • N. Dvornak et al.

    Housing wealth, stock market wealth and consumption: a panel analysis for Australia

    Econ. Rec.

    (2007)
  • Ellis, L., Lawson, J., Roberts-Thomson, L., 2003. Housing Leverage in Australia, RBA Research Discussion Paper No...
  • L.G. Epstein et al.

    Ambiguity, information quality, and asset pricing

    J. Finance

    (2008)
  • D. Genesove et al.

    Loss aversion and seller behavior: evidence from the housing market

    Quart. J. Econ.

    (2001)
  • Cited by (15)

    • Valuation of energy efficient certificates in buildings

      2018, Energy and Buildings
      Citation Excerpt :

      Basically, the economic issues related to green buildings can be classified into building costs (the negative side) and property benefits (the positive side). According to Morrison Hershfield’s report on the business case for green buildings, the negative building costs include: (i) Direct capital costs; and (ii) Direct operating costs, while the positive property values include: (i) Productivity benefits; (ii) Property absorption rates; (iii) External or tertiary benefits (such as the reduced greenhouse gases and the reduced reliance on infrastructure); and (iv) Other indirect or intangible benefits (such as the increased retail sales and high quality) [35]. When producing and consuming the energy labeled products, both builders and buyers need to consider whether the money on the energy labels is worth for the value in return.

    View all citing articles on Scopus

    We are gratful to Alexandra Heath, Greg Kaplan, Christopher Kent, Katherine Leong, Bruce Preston, Tony Richards and Peter Tulip, as well as Roger Wilkins and other seminar participants at the HILDA survey Research Conference 2013. The views expressed in this paper are those of the authors and do not necessarily reflect the views of the Reserve Bank of Australia. The authors are solely responsible for any errors.

    View full text