The previous section discussed that endogeneity and contemporaneous correlation may be at hand in the fixed effects models. This could be due to a number of reasons. Government revenue is generated from housing sales or the value of a dwelling that is owned. Therefore, not only does government revenue affect house prices, but house prices also determine government revenue. A couple of variables demonstrate differing effects once time fixed effects are included—namely, municipality rate revenue and PPI. These factors may indicate the presence of endogeneity. This section reports IRFs generated using PVAR following Love and Zicchino (2006).
Figure 2.4 depicts the IRFs of key variables to a one-period lag shock to house prices. House prices are of importance to stamp duties and municipality rates in the middle-right and middle-left panels respectively. Interestingly, a shock of house prices has a negative effect on stamp duties at one period. However, the effect of this shock flattens over five periods. This contrasts with municipality rate revenue. The shock to municipality rate revenue is positive at one period, albeit very small. Volatility is present for a number of periods after this point until it dampens considerably around five periods.
Figure 2.4: One-period lagged response of change in key variables to a shock in change of house price
Notes: Errors are a 5 per cent on each side, generated by Monte Carlo with 1000 reps.
Land taxation revenue, top-right panel, also demonstrates positive growth at one period in response to a shock in the growth of house prices. However, the error bands surrounding this result are quite large. This effect turns negative at around two periods. Real interest rates appear to fall with a positive shock to house prices. Given the Reserve Bank of Australia’s reluctance to engage in macroprudential policy—like monitoring indicators for credit and asset prices, liquidity, lending standards and housing market
imbalance to manage financial systemic risk7 (RBNZ 2017; Lim et al. 2011)—over this
period (Ellis 2012), this response is likely due to monetary policy transmission in response to economic events exogenous to the housing market.
Two things are of particular interest in the bottom two panels of Figure 2.4. First, house prices appear to have an instantaneous large negative shock due to lagged house price shock, which flattens quickly. This could be reflective of the market supply of existing dwellings. As house prices rise, the equity that homeowners hold also rises. Thus, a short increase in house prices could cause households who were considering selling to hold off until the next period.
Second, the marked contrast of dwellings completed (bottom-left panel in Figure 2.4) with an initially large positive effect after a house price shock. This positive growth becomes negative after two periods. Volatility in dwellings completed continues for approximately four periods in the future. The volatility and the movement of these shocks show that there may be pent-up demand for future dwelling stock. This may be due to a shortage of housing. Additionally, developers releasing parcels of land in blocks rather than over extended periods could be driving these results.
The PVAR results in Figure 2.5 examine how changes in house prices are affected by a one-standard-deviation lagged shock of key variables used in the fixed effects analysis. Lagged shock to stamp duty on conveyance revenue (middle-right panel of Figure 2.5) appears to be positively associated with house prices growth. Lagged changes to real stamp duty revenue has a large spike in the first period of around 20 before a large correction to approximate 2 at period two. It then stabilises around 0 over time.
7 After identifying systemic risk using macroprudential indicators, the RBNZ deploy macroprudential instruments such as the countercyclical capital buffer, adjustments to the core funding ratio, adjustments to sectoral capital requirements or quantitative restrictions on the share of high loan-to-value ratio loans to the
Figure 2.5: One-period lag response of change in real house price to a shock in key variables of interest
Notes: Errors are a 5% on each side, generated by Monte Carlo with 1000 reps.
Land tax revenue (top-right panel of Figure 2.5) is positive during most of the impulse response function. However, there is a great deal of volatility in earlier periods. House prices are initially negative at one period following a shock to land tax revenue, this effect turns positive by period two. With continued volatility over the next two periods the effect dampens out over time.
Municipality rate revenue shocks also demonstrate a positive relationship with change in real house prices. This shock dampens out gradually over time.
A shock to real interest rates (top-left panel of Figure 2.5) has a decreasing positive effect on house prices over a long period. Interestingly, there is a dramatic positive relationship in the middle of this otherwise gradual slope. This could feed into the result observed in the fixed effects model. In the long-run, housing markets respond to interest rates in a rational way. However, short-run deviations in mortgage rates could be capitalised into house prices. The change in housing stock also appears to follow a similar pattern considering the PVAR results for dwellings completed.
Dwellings completed (bottom-left panel of Figure 2.5) shows a moderately negative effect transmitted over two periods before a slight positive correction occurs. This gives credit to the possibility that developers can profit off inelastic housing markets, contributing to growth in house prices. This demonstrates the importance of getting the incentives right in the housing market as a society can bear a great cost if these incentives are misaligned. Malpezzi and Mayo (1999) highlight this in their study of housing supply in Malaysia.
Appendix D2, Figure D2.1, postulates a different order and the inclusion of control variables used in the fixed effects analysis. Only land tax, as the largest impacting government revenue variable identified in the fixed effects analysis, was included in this model. We use the ordering of house prices, interest rates, dwellings completed, real gross state product, PPI, unemployment, land tax and population. The figure reports the impulse responses for each variable. The findings indicate that a shock to dwellings completed, real gross state product, PPI, unemployment rate and population all have a positive effect on house prices. Interest rates also have a short positive effect on house prices, although this turns negative after one period before tapering out. Land tax has an immediate negative effect in this instance. Given the volatility of land tax shown in the above Figure 2.5, this result is not completely surprising. It may be likely that, given land tax is predominately levied at landlords, the increased cost of owning investment dwellings causes investors leave the market. This gives way for owner occupants to enter, which may contribute to the positive effect on prices that is observed in the above results. Overall it appears that government revenue has a positive relationship with house prices in Australia. This supports the hypothesis that the revenue extracted from housing markets in Australia increase house prices similar to how additional regulatory burden
aligned with the wider literature. The more government impacts on the housing markets, the harder it is for households to allocate themselves efficiently. This arises through increased transaction costs for homeowners and developers incurring greater regulatory burden making it relatively more difficult to match changing market dynamics (Glaeser, Gyourko & Saks 2005; Huang & Tang 2012).