RDP 2018-04: DSGE Reno: Adding a Housing Block to a Small Open Economy Model 1. Introduction

Economic activity in the Australian housing market has increased substantially over the past five years, attracting considerable interest from academics and policymakers.^[1] Housing investment, for example, has grown by about 30 per cent since the end of 2012 (Figure 1). The expansion in housing investment has helped to partially offset the large decline in mining investment associated with the downswing in the recent commodity price cycle. More generally, the housing market has played an important role in the transition of the economy away from the mining boom (e.g. Lowe 2015).

Against this background, the goal of this paper is to explicitly capture housing investment dynamics within a multi-sector model that is fit to the Australian economy. It is important to explicitly account for the housing sector, as there are many economic reasons to think that the housing sector, and in particular housing investment, may behave differently than other non-tradeable goods and services sectors. For example, Lawson and Rees (2008) find that investment in housing plays a particularly significant role in the transmission of monetary policy in Australia.

Moreover, research on housing and the macroeconomy shows that movements in housing markets are not just the consequence of wider macroeconomic developments, but can also be important impulses to business cycle fluctuations. Iacoviello and Neri (2010), for example, study housing in a multi-sector estimated dynamic stochastic general equilibrium (DSGE) model and conclude that shocks emanating from the housing market have noticeable effects on the real economy, especially on consumption. Muellbauer (2015) cites evidence from several model-based and econometric studies to argue that housing is a significant determinant of inflation in the United States.

Our starting point is the model in Rees, Smith and Hall ((2016); henceforth RSH). The RSH model is a multi-sector DSGE model designed from the ground up to explain aggregate variables that are relevant to the Australian economy and to capture the relative importance of, and interaction between, different sectors within a small open economy setting. It complements the Reserve Bank of Australia's suite of economic models, and has been in use for a few years as an analytical tool to conduct scenario analysis. We leave the key features of the RSH model intact, but incorporate a standalone housing sector into the existing framework.

To do this, we propose a general and straightforward modelling approach that may be applied to a wide variety of existing DSGE models. The approach generates a number of intuitive predictions, such as an increased sensitivity of housing investment to monetary policy, but leaves the aggregate predictions of the original model broadly unchanged. Specifically, we modify the RSH model along two dimensions. On the production side, we split the non-tradeable sector into a housing services sector and a non-tradeable excluding housing sector. On the demand side, we assume that households gain additional utility by holding housing stock. Therefore, in our framework, the housing stock is used by the housing services sector, along with other inputs, to produce housing services that form part of agents' consumption bundle. In addition, the housing stock also enters agents' utility function directly, separate from consumption.

The separation of the consumption of housing services from investment in the housing stock has a number of advantages. First, it allows for greater consistency between key macroeconomic variables such as GDP and CPI inflation, both of which have a large weight placed on housing services, and their model counterparts.^[2],[3] This consistency is particularly important in our application as the model is primarily used for scenario analysis (examining the implications of different scenarios for key macroeconomic variables), and so we want the dynamics and definitions of the model variables to be as similar as possible to those of their real-world counterparts.

The second advantage of the separation is that it allows us to differentiate changes in the level of the housing stock that are driven by changes in the supply of and demand for the stock, from those driven by changes in the productivity of the housing stock in producing housing services. In turn, this also allows us to consider a broader range of scenarios, including: changes in the demand for the housing stock due to changes in tax treatments, which influence the post-tax return on housing and therefore households' willingness to hold housing assets; and increases in the productivity of the housing stock due to increased density of the housing stock, which would imply that, for a given amount of housing investment and capital, more housing services could be generated. The latter scenario is particularly interesting in the context of Australia, where the recent expansion of housing investment has been concentrated in the high density segment of the market.

Our preference assumption has implications for both the steady-state and dynamic properties of the model. First, because households gain utility from holding housing stock, they require a lower rate of return in steady state compared to the case of no housing stock in the utility function. This implies a higher level of housing stock in steady state, which is a key feature of the data. Indeed, without the assumption that the housing stock appears in the utility function, our model has difficulty matching the relative proportion of housing held by households in the data. Second, housing investment is more responsive to changes in the real interest rate compared to the case without housing in the utility function, which is consistent with empirical evidence on the relatively high sensitivity of housing investment to changes in interest rates (e.g. Lawson and Rees 2008). Finally, including the housing stock in the utility function also generates a positive correlation between real housing wealth (measured by the housing stock) and consumption. This is qualitatively similar to the housing wealth effects documented empirically in Australia by Dvornak and Kohler (2007) and Gillitzer and Wang (2015), and to the effect of the collateral constraints included in Iacoviello (2005) and Iacoviello and Neri (2010).

There are a number of justifications for treating the housing stock differently from other forms of capital and including it directly in the utility function as well as assuming that it is an input into housing services. One justification is the empirical observation that households tend to treat housing differently to other assets. Home ownership – in Australia and in many other developed countries – makes up a significant proportion of household wealth, and it is well-known that households' decisions with respect to their housing wealth often differ from the standard predictions of models of consumption and saving. For example, households typically dissave their housing wealth in retirement at rates below what the classical life-cycle model would predict, which Skinner (1996) argues could reflect the use of housing wealth as a form of self-insurance. Meanwhile, Nakajima and Telyukova (2013) use cross-country data and find that households tend to not use their housing wealth to respond to income shocks in the same way as they do other assets, which suggests that housing is not accumulated for precautionary savings motives in order to smooth consumption in the same way as other assets.

Another justification for including the housing stock in the utility function is the differential tax treatment of housing assets in Australia. In Australia, landlords can deduct expenses related to investment properties from their gross income, whereas expenses incurred in operating a business are generally quarantined to business income.^[4] Meanwhile, owner-occupier housing receives preferential capital gains tax and pension assets test treatment, relative to other assets. Since we do not model a detailed fiscal sector, including the housing stock in the utility function acts as a proxy for these taxation features by providing households with an extra incentive to invest in and accumulate housing.^[5]

Compared to the existing literature on the modelling of housing, our approach shares some similarities with Iacoviello-type models, which are used extensively in the literature (e.g. Iacoviello 2005; Iacoviello and Neri 2010). In these models, the housing stock also enters agents' utility function directly. However, it enters as a second consumption good to reflect housing services, which are assumed to be directly proportional to the housing stock. In our model, the housing stock is used to produce housing services, which are incorporated into the consumption bundle, but it also provides utility directly. Therefore, while preferences have a similar functional form in Iacoviello-type models, the interpretation is slightly different in our approach.

The other main difference is that in Iacoviello-type models housing serves as collateral for loans, whereas we abstract from debt dynamics implied by liquidity constrained households, instead focusing on housing investment decisions. We do so to maintain the standard representative agent framework and keep our model consistent with RSH. Although this simplification prevents us from incorporating collateral effects – likely to be important in trying to satisfactorily model the price of the existing housing stock, which is the price of interest for the housing sector in Iacoviello-type models – we show that our model does a good job in capturing aggregate and housing sector dynamics. Therefore, our specification represents a parsimonious, but still effective way to capture housing investment dynamics within a multi-sector model.^[6]

We estimate the model using standard Bayesian techniques. We test its out-of-sample forecast accuracy using a pseudo forecasting experiment. We find that the model has reasonable out-of-sample forecast properties, especially for housing investment and value-added growth. While the model is not intended to be used for forecasting, its reasonable forecast performance provides evidence that it adequately captures relevant economic dynamics.

We also compare the impulse response functions of our model to those arising from RSH and from structural vector autoregression (SVAR) models for monetary policy shocks. Relative to RSH, the introduction of the housing sector increases the persistence of the output response to monetary policy shocks, while the magnitude of the response of inflation is relatively unchanged. These results are consistent with evidence arising from SVAR models estimated with the same disaggregated data.

We then explore the model dynamics associated with the different shocks in the housing sector. We show that shocks to the housing services sector have quite different effects on the economy than shocks that affect the stock of housing directly. In particular, negative shocks to the productivity of housing services tend to be inflationary, whereas negative shocks to the housing stock (housing preference or investment shocks) tend to be deflationary.

Finally, we use the model to assess the contribution of the housing sector to aggregate GDP growth and CPI inflation since the end of the mining boom in 2012. We find that housing investment played a significant role in driving growth and inflation over the past five years, adding a ½ percentage point to GDP growth and a ¼ percentage point to inflation, both in year-ended terms. We also find that the significant increase in housing investment has been primarily driven by the endogenous response of monetary policy to negative commodity price movements. In other words, monetary policy has largely been implemented as one would have expected based on past policy decisions and the fall in output and inflation observed during this period. Meanwhile, shocks emanating from the housing sector have actually weighed on housing investment, which means that housing investment has been lower than what the model would have predicted given the macroeconomic environment. This could reflect, for instance, capacity constraints in the sector or even the effect of changes in lending standards in recent years.

Footnotes

See, for example, Fox and Finlay (2012), RBA (2015), Lim and Tsiaplias (2016) and Shoory (2016), among others. [1]

Iacoviello (2005) and Iacoviello and Neri (2010) both use aggregate consumption data as an observable for consumption excluding housing, which creates an inconsistency between model variables and their real world counterparts. Moreover, both papers focus on the quantity and price of the housing stock, not housing services, with the latter being the relevant concept for GDP and CPI inflation. [2]

The current weight of the housing component of the CPI basket is about 23 per cent. The average housing share of nominal gross value added from 1993 to 2016 is around 16 per cent. [3]

Interest on ‘margin loans’ used to purchase equities is treated similarly to interest on housing loans. However margin lending is much less prevalent. [4]

Similar complicated tax arrangements exist across the developed world and typically are not modelled in DSGE settings. [5]

It is worth noting that our model can be further extended to incorporate heterogeneous households and borrowing constraints. This is an important task for future research. [6]