may also have the opposite effect, if renters feel that buying a home is increasingly out of reach, they give up saving for a home, and increase their consumption instead. Therefore, in this subsection, we conduct our cross-sectional and pseudo-panel analyses separately for homeowner households and renter households as a robustness check of our wealth effect estimates.

Table 5 shows the regression results based on the cross-sectional data when the sample is confined to homeowners. In specifications (2) and (6), the MPCs out of housing wealth are 0.0010 (nondurable consumption) and 0.0027 (total consumption), which are significant but slightly smaller than the corresponding estimates in Table 2, which are based on the sample including renters. Further, as before, we find that the estimated MPCs are larger for older than for younger households, which is consistent with the LC/PIH (pure wealth effects hypothesis).

Table 6 summarizes the regression results based on the cross-sectional data when the sample is confined to renters. Since housing wealth for renters is zero, we cannot simply run a regression using housing wealth as an independent variable. To circumvent this issue, we replace it with the average housing wealth in the city where renters reside. According to the estimates using specifications (2) and (6), the MPCs out of housing wealth are 0.0002 (nondurable consumption) and 0.0011 (total consumption). Both values are statistically significant and positive, but very small. This means that renters living in a region where the cost of purchasing a house is higher than in other regions tend to consume slightly more, holding all other factors constant. At first glance, this might appear consistent with an expectations-based explanation, a variant of the third-factor hypothesis. That is, positive news regarding future income simply simultaneously boost both spending by renters and land prices. However, this cannot be the case, since the MPCs for young households are smaller (not larger) than for older households, which contradicts the prediction derived from the third-factor hypothesis.

There are at least two ways to interpret the result. The first possibility is that renters faced with higher house prices tend to consume more simply because they have given up on buying their own home one day and increase their non-housing expenditure instead. If this tendency is observed more strongly for older than for younger households, this interpretation could be a possible explanation for the estimation results in Table 6. Another possibility is that the statistically significant positive estimates in Table 6 are spurious and contaminated by omitted variable biases. To examine which interpretation is correct, in specifications (4) and (8) we further confine the sample to those aged 65 or older and find that the MPCs are no longer statistically different from zero. This suggests that the estimates in specifications (2) and (6) are possibly upwardly biased due to the omission of permanent income. Although it would be worthwhile to investigate in more detail which of the possible interpretations is more reasonable, we leave this issue for future research, since renters' response to changes in housing prices is not the main focus of our study.

It is also possible to confine the sample to homeowners and create pseudo-panel data just as in Section 4.2. (On the other hand, the number of renter households is too small to construct a pseudo-