Changes in Risk Appreciation, and Short Memory of House Buyers When the Market is Hot, a Case Study of Christchurch, New Zealand

Many property markets around the world post COVID-19 lockdowns were dominated by significant price inflation due to low interest rates. However, there is preliminary evidence of more complex changes than just an increase in prices. For example, some buyers seem to appreciate larger sections after having been in lockdown. In this paper we analyze hedonic prices and marginal willingness to pay of house buyers for different features of a property using the hedonic house price model. We compare the property market over three periods. Relatively stable periods during 2014 to 2016, the period pre COVID-19, and a hot market post COVID-19. We find that hedonic prices change and buyers seem to have a different attitude towards risk associated with the location of the house.

One of our main findings is that in the period immediately following the 2011 earthquake, buyers were willing to pay more for locations that were known to have a relatively low risk of sustaining damage due to soil liquefaction in the event of another major earthquake. In the other periods, 9 to 10 years after the major Christchurch earthquake, the effects of this additional risk to the property was not as significant. We found that, post-lockdown, buyers were willing to pay more for a larger section size, compared to the pre-lockdown period.

The hedonic house price model is a very popular tool and has been widely used in the literature. It builds on hedonic price models for differentiated products introduced by Lancaster, (1966). These models explain product prices as a function of observable product characteristics. Rosen, (1974) connects the model to consumer demand in an equilibrium market which allows to infer the consumer’s marginal willingness to pay for each product characteristic from observations of prices. The main assumptions necessary for this deduction are a market equilibrium, i.e. no consumer can increase their utility by choosing a different product. In addition, consumers are fully informed, free to choose any other product and to purchase a continuous level of each characteristic. For detailed discussions of these assumptions in the context of property markets see Palmquist, (2005).

A large number of studies did use hedonic price theory to analyze the property market. We can just mention a few. Heteroskedasticity in the hedonic price model induced by dwelling age is explored in Goodman and Thibodeau, (1995) which describes a vintage effect that drives up a house’s price. An extension is Fletcher et al., (2000) which identifies other possible sources of heteroskedasticity, specifically the area of the property. Another important work using the hedonic price model is spatial autocorrelation and spatial heterogeneity. In Dubin, (1992), using data from Baltimore, a new approach to evaluating the strength of the effect of spatially related variables such as neighbourhood quality and accessibility is implemented. The method builds on the hypothesis that the spatial auto- correlation in the error term will reveal the effect of neighbourhood quality and accessibility when they are left out as initial explanatory variables. In Helbich et al., (2014), the importance of spatial heterogeneity is demonstrated in the case of Austria, where it was shown that effects of variables can be overestimated if spatial effects are not considered. The hedonic house price model has also been used to examine the effects of specific characteristics of a house on its price. The effect of transportation infrastructure on house prices in Sydney, Australia was examined in Lieske et al., (2021). Effects of architectural features were analyzed with a spatial differences in differences method by Ahlfeldt and Holman, (2018). The effect of upzoning on house prices in Auckland, New Zealand was examined Greenaway-McGrevy et al., (2021). And, the effects of school zones and proximity to cellular towers on house prices in New Zealand was examined in Rehm and Filippova, (2008), and Filippova and Rehm, (2011). In Limsombunchai, (2004) the hedonic model was compared to an Artificial Neural Network in terms of predictive capability based on Christchurch data. Finally, the impact of earthquake risk on property prices in Japan has been analyzed in Ikefuji et al., (2022).

The outline of the paper is as follows. Section 2 gives an overview about the data sources and quality. The model is explained in Section 3. Section 4 discusses different findings. The paper closes with in conclusion in Section 5. The Appendix contains further discussions of spatial and quarterly dummy variables.

The data consists of log real house prices and a selection of structural, neighborhood, and locational characteristics. The data were obtained from variety of different sources. Data such as nominal sales prices, dates of sale, structural characteristics, and zoning were obtained through TradeMe and QV. Real house prices are obtained by dividing the nominal house prices by the House Price Index (HPI) obtained from the Reserve Bank of New Zealand. Geospatial related characteristics such as distance to schools, and the CBD were obtained by geocoding the addresses through the Googlemaps API and calculating the Haversine distance between two pairs of longitude and latitudes.

Another very important geospatial characteristic is the technical category (TC) of the land on which a property is built on. The technical category is a classification of land that indicates the likelihood of a property to experience soil-liquefaction related damage in the event of an earthquake. TC1 zones are the least likely to sustain liquefaction related damage, and TC3 zones face the possibility of significant damage related to liquefaction in the future event of a significant earthquake, with TC2 zones in between. These data were obtained from Canterbury Maps Open Data.

The 2013 and 2018 New Zealand Deprivation Indices, which are comprised of variety of measures of deprivation such as income, education, and housing quality, Atkinson et al., (2021), were used as features that represent the overall condition of a neighbourhood. The index was used as a proxy variable for how good the area is to live in. The deprivation index is available at a meshblock level which is the smallest geographical unit for which statistical information is collected, see Atkinson et al., (2021).

The 2010 New Zealand school zones were obtained from Koordinates, which is an open access database for geographic data, as a geographical comma delimited file. The geocoded properties were then assigned using their latitude and longitudes by determining if it lies within the polygonal area of a specific school zone. In 2019, new demarcations for school zones were issued and initially made known. These changes have not taken effect as of 2021. The data is also not yet available in an open access format. However, the changes may have already affected property prices. We kept this in mind when comparing the effects of school zones across different time frames.

The data was cleaned and filtered before it was used for model fitting. Three subsets were constructed based on different time periods, 01-Jan-2014 to 31-Dec-2015, 01-March-2019 to 29-Feb-2020, and 01-May-2020 to 30-Jun-2021. The first time period corresponds to a time frame that is close to the 2011 earthquake that caused significant damage to a large number of properties in Christchurch, but is a period where the property market returned to stable conditions. The second time period corresponds to a time frame that is temporally distant from the 2011 earthquake and before the 2020 nationwide lockdown due to COVID-19. The final time period corresponds to a time period after the easing of lockdown restrictions and when the market was turning hot.

To ensure that there were no erroneous entries and significant outliers, sensible filters were applied. Properties whose distance to the CBD was greater than fifteen kilometers were removed to ensure only properties within Christchurch were considered. Properties with floor areas less than $60m^{2}$, land areas less than $200m^{2}$, and land areas greater than $3000m^{2}$ were removed.

Furthermore, only ’Residential Suburban’ properties were used. This was done in order to control for the unobserved effects attached to different zoning codes, as it carries with it different rules for land use. Properties used in the analysis were also limited to those that have assigned technical categories. The summary of features used as input data for the pre-lockdown period can be seen in Table 1, the additional tables for other time periods can be found in the Appendix.

Finally, there is an important consideration to make when drawing conclusions derived from the subset of data spanning 2014 to 2016. Due to constraints and limitations in the process of obtaining the data, we were limited to using properties that were sold both in the period 2019 to 2021 and 2014 to 2016. This leads to some form of a selection bias, since there might be particular qualities that are associated with properties that are resold at a higher frequency. However, the effect of this is not within the scope of the study, and we assume the case that the selection bias has minimal impact on the specific analysis done in this paper.

The first model considered is the standard multiple linear regression model given by the equation below,

where $\mathbf{X}$ is a matrix of regressors listed in Table 1, ${\bm{\beta}}$ is a vector of slopes and an intercept, $\boldmath{\epsilon}$ is the error term, and $\boldmath{P}$ are the real property prices.

An alternative approach is to use nominal instead of deflated log-prices as depended variable and account for price inflation by adding time dummies to the regressors, see Tripplett, (2004). We present the results of the related time dummy model in Appendix C. As one would expect, the results are similar to using real prices.

An important consideration in fitting the hedonic model is omitted-variable bias that manifests as spatial correlation, Bishop et al., (2020). One variable of interest, technical category (TC) is an inherently spatial characteristic of a residential property. Great care was taken in specifying the model to prevent misattributing variation in the response variable to TC rather than other spatial characteristics. However, it is unlikely that all attributes of a property that are geographically determined can be accounted for.

An approach suggested by Bishop et al., (2020) to address the problem of omitted-variable bias is to assign a dummy variable that determines the geographical location of a property such as as a zip code, or a suburb. We follow the methodology in Gibson et al., (2007) where the wards in which properties are in were included as a variable.

We have investigated the use of suburbs as the geographical feature mainly used to capture additional variation in the response due to geographical location. While the use of suburbs does better specify the model indicated by an increase in the model’s R-squared it also introduces the problem of collinearity with the TC variable, and hence masks its effect. More specifically, in the 2020 to 2021 period subset of data, all TC1 classified properties are found in only thirteen out of the sixty-five suburbs, and out of all the properties located in these suburbs included in the data set 74% are TC1. The two other subsets of data for the 2014 to 2016, and 2019 to 2020 periods have similar statistics, as is shown in Table LABEL:tab:contigency in Appendix D. Given that this masks the effect of the variable that we are interested in, we opted to retain the usage of wards, distance to the city center and secondary schools, and the deprivation index as the controls to account for variation in the response due to geographical location. A summary of the models, and the relevant contingency table is available in Appendix D.

It was made apparent in McClay and Harrison, (2003), Gibson et al., (2007), and Rehm and Filippova, (2008) that school zones are a significant driver of house prices in New Zealand. In our analysis, Burnside, Riccarton, Christchurch Girls’, and Christchurch Boys’ school zones were considered as in Gibson et al., (2007), and additionally we also included the Cashmere Highschool zone. These school zones were used as variables to account for the premium commanded by being located in these desirable school zones.

Ideally other environmental amenities would be included such as air quality and water quality as geographical features; however, these data are unavailable at the desired level of granularity.

The results for the hedonic regression models are presented in Table LABEL:tab:regression_results. All the models for the periods 2014 to 2016, 2019 to 2020 (pre-lockdown), and 2020 to 2021 (post-lockdown) were significant at a 0.001 level. The models are all well-performing as each had an $R$-squared of 0.751, 0.808, and 0.823 respectively.

We focus our investigation on shifts in buyers’ marginal willingness to pay across the different time periods for certain structural attributes, changes in the assessment of risk of sustaining earthquake damage brought about by a property’s location. Locational and neighborhood attributes such as school zones, deprivation index, and wards primarily serve as control variables to prevent misattribution of variation to the locational variable of interest, technical category (TC). In-depth discussion on the effects of these control variables across the different models can be found in Appendix B.

Analysis of the effect of school zoning on house prices in Christchurch has been done in McClay and Harrison, (2003), and Gibson et al., (2007). The results that we obtained generally agree with the findings in those studies. We assume that the results in these studies are more precise than our results on school zones, because the models used in McClay and Harrison, (2003) and Gibson et al., (2007) are specifically designed for analyzing school zones, while our focus is on the effect of the technical categories.

When interpreting the results it needs to be noted that changes in implicit prices can have several sources. One source can be a change in buyers’ valuation of the attributes. A second effect can come from a shift in supply or demand which leads to a new market equilibrium. The first two periods in our study saw a relatively stable property market in Christchurch with almost flat price increases. We assume that the market equilibrium did not shift much between these two periods. The third period was characterized by decreased interest rates, increased demand, and rising prices. We assume that changes of hedonic prices in this period are driven by both, the new situation in the market, and a change of buyers’ valuation of certain attributes after going through the experience of a lockdown.

In this case study of the Christchurch housing market we applied a hedonic house price model to analyze the willingness to pay for attributes of properties in three different time periods.

We observed significant shifts in hedonic prices across these three different time periods: 2014 to 2016, 2019 to 2020 pre COVID-19 lockdown, and 2020 to 2021 post COVID-19 lockdown.

In 2014 to 2016, a period relatively close to the 2011 earthquake, we found that a property’s locational attribute, the technical category (TC), which quantifies the risk of sustaining damage from soil liquefaction in the event of an earthquake, was a highly significant driver of house prices. The results showed that during this period a classification of TC3, which indicates the highest risk of damage among the TC classifications, reduces a buyer’s marginal willingness to pay by 21.9% on average, and a classification of TC2 reduces it by 11.9% on average. This changes in the following two periods which are much more temporally distant to the 2011 earthquake. In the pre COVID-19 period, there is no evidence that buyers distinguish between TC1 and TC2 properties, and the effect of a TC3 classification on a buyer’s marginal willingness to pay was significantly lower compared to the previous period. In the post COVID-19 lockdown period the effect of TC changed once again. There is no evidence that buyers distinguish between TC2 and TC3 properties in this period. These changes suggest that over the years buyers have likely started to care less about the risks associated with a property’s TC classification. In addition, our estimates for the effect of school zones is in agreement with the findings of McClay and Harrison, (2003), and Gibson et al., (2007).