A Model of the Australian Housing Market RBA

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The Discussion Paper series is intended to make the results of the current economic research. within the Reserve Bank available to other economists Its aim is to present preliminary results of. research so as to encourage discussion and comment Views expressed in this paper are those. of the authors and not necessarily those of the Reserve Bank Use of any results from this paper. should clearly attribute the work to the authors and not to the Reserve Bank of Australia. Phone 61 2 9551 9830,Facsimile 61 2 9551 8033,Email rbainfo rba gov au. Website https www rba gov au, Figures in this publication were generated using Mathematica. The contents of this publication shall not be reproduced sold or distributed without the prior. consent of the Reserve Bank of Australia and where applicable the prior consent of the external. source concerned Requests for consent should be sent to the Secretary of the Bank at the email. address shown above,ISSN 1448 5109 Online,A Model of the Australian Housing Market. Trent Saunders and Peter Tulip,Research Discussion Paper. March 2019,Economic Research Department,Reserve Bank of Australia.
We are especially grateful to Glenn Otto for detailed thoughtful comments Thanks also to. Tom Cusbert Peter Downes Luci Ellis Liz Kendall Matt Larkin Dominic Meagher Ewan Rankin. Dan Rees Tom Rosewall Paul Ryan John Simon Luke Willard and seminar participants at the. Reserve Bank of Australia Treasury the Grattan Institute and the University of New South Wales. 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. Online Appendices EViews programs and data are available at. https www rba gov au publications rdp 2019 2019 01 supplementary information html. Authors saunderst and tulipp at domain rba gov au,Media Office rbainfo rba gov au. We build an empirical model of the Australian housing market that quantifies interrelationships. between construction vacancies rents and prices We find that low interest rates partly reflecting. lower world long term rates explain much of the rapid growth in housing prices and construction. over the past few years Another demand factor high immigration also helps explain the tight. housing market and rapid growth in rents in the late 2000s A large part of the effect of interest. rates on dwelling investment and hence GDP works through housing prices. JEL Classification Numbers E17 R30 R31, Keywords housing construction house prices vacancies rents. Table of Contents,1 Introduction 1,2 Previous Research 2. 3 Overview of the Model 2,4 Main Equations 4,4 1 Building Approvals 4. 4 2 Other Construction Variables 11, 4 2 1 Dwelling investment and the housing stock constant prices 11.
4 2 2 Completions and the number of dwellings 13,4 3 Rental Vacancies 14. 4 4 Rents 17,4 5 Housing Prices 18,4 6 Other Equations 24. 5 Model Responses 24,5 1 Responses to Interest Rates 24. 5 2 Responses to Population Growth 27,5 3 Responses to Completions 28. 5 4 Responses to Changed Price Expectations and the User Cost 29. 6 Conclusion 31,References 32,1 Introduction, The Australian housing market shows strong relationships between interest rates investment rents.
and prices This paper combines these relationships in one hopefully realistic model The model. provides internally consistent projections for housing construction prices and rents It estimates. responses to interest rates allowing for feedback between quantities and prices It helps explain. historical developments It can inform housing policy and taxation policy. These key relationships include, a Interest rates income and housing prices have strong and clear effects on residential. construction, b Dwelling completions and changes in population explain the rental vacancy rate. c The vacancy rate has a strong and clear effect on rents. d Interest rates rents and momentum have large effects on housing prices. e Housing prices and construction are mutually determined so examining bivariate relationships. in isolation can be misleading, Some of these observations are not new However most have not been estimated or publicly. documented for a long time if at all Nor have their interrelationships been explored This paper. aims to fill those gaps In doing so we re examine these relationships in the light of recent research. and data This gives us a system of equations that for the most part fit the data fairly closely are. stable across time and are consistent with theory and previous research There are some exceptions. to this which we note below,Our model quantifies some important developments. The model suggests that much of the strength in housing prices and construction over the past. few years can be explained by the fall in interest rates some of this fall reflects lower world real. interest rates and some is cyclical, A large part of the effect of interest rates on dwelling investment and hence real GDP occurs.
through the channel of housing prices, The model suggests that an increase in population growth will reduce rental vacancies boost. rents and housing prices and increase construction This helps to explain developments following. the immigration surge of the mid 2000s, The model is consistent with some important longer run trends Construction activity is. approximately cointegrated trends together with income although the housing stock is not The. rental yield is cointegrated with the user cost of housing Rents tend to grow slightly faster than. inflation but slower than income per capita,2 Previous Research. Most empirical research into the Australian housing market has focused on individual equations For. example Bourassa and Hendershott 1995 Abelson et al 2005 and Tumbarello and Wang 2010. examine housing prices McLaughlin 2011 and Gitelman and Otto 2012 examine housing. construction The IMF 2018 estimate and discuss equations for both variables separately Our. approach differs in that we put these and other equations together We also re examine this earlier. work in the light of recent research and data, Another strand of research has examined the housing market as part of structural macroeconometric. models Examples include Jovanoski Stoney and Downes 1997 Powell and Murphy 1997 or the. Reserve Bank of Australia s RBA s new MARTIN model Cusbert and Kendall 2018 Our approach. differs in that we look at the housing market in more detail including variables that these models. often exclude such as building approvals completions or the vacancy rate The macroeconomic. models are designed to settle down to an explicit steady state with simple properties such as. constant relative prices or expenditure shares That facilitates the models application to a wide. range of questions However in discussing housing specific issues it is not desirable to have. important results driven by assumptions for which evidence is weak We do not constrain the steady. state of our model except when the data suggest this is realistic. These alternative approaches have advantages relative to our approach Larger models allow more. variables to be endogenous allow for feedback and can answer a wider range of questions Smaller. models are less reliant on chains of causation which can be as fragile as their weakest link And. more focused models allow examination of specific estimates in more detail These different. approaches are complementary When a range of different approaches support similar results we. have more confidence in the conclusions Accordingly we discuss specific points of agreement and. disagreement where they arise in the discussion, In common with most previous Australian research on housing markets and most macroeconomic.
forecasting we focus on single equation least squares estimates using aggregate quarterly data. Identification is typically through lags and a priori reasoning For example rents are explained by. lagged vacancies and contemporaneous income on the assumption that the right hand side. variables are weakly exogenous Given that the future does not determine the past that strikes us. as plausible for most lagged variables This argument is less compelling when the right hand side. variables are forward looking such as investment or asset prices However we are not aware of. clear evidence or strong arguments that weak exogeneity fails in the relevant equations or of useful. instruments that might rectify this One alternative to our approach would be to assume model. consistent expectations but that seems unrealistic We recognise that finding X regularly precedes. Y does not prove that X causes Y However in the absence of reasonable arguments to the. contrary the latter statement seems a natural hypothesis to maintain pending more definitive tests. This approach essentially that of structural macroeconometric modelling rests on methodological. assumptions that are controversial but that debate is outside the scope of this paper. 3 Overview of the Model, Our model takes income interest rates population and depreciation as given More precisely these. variables are modelled simply with no feedback from the housing sector We examine how these. and other factors combine to influence construction activity the housing stock rents and housing. prices Figure 1 shows a stylised summary of our model with a more detailed version presented in. Online Appendix A This framework is more detailed than but not conceptually different to the. housing blocks of the Australian macroeconometric models cited above Oxford Economics 2016. Figure 1 describe their model of the UK housing market with a similar diagram. Figure 1 Key Model Relationships, Note Numbers in parentheses denote the section where the relevant equation is discussed. The model has six important equations denoted by blue boxes which we discuss in Section 4 It. also contains a large number of identities and simple forecasting equations typically. autoregressions used for projecting forward variables we think of as exogenous The equations are. presented in Online Appendix E available in the supplementary information published with this paper. on the RBA website,4 Main Equations,4 1 Building Approvals. We model constant price building approvals for detached houses higher density housing and. alterations and additions separately 1 This disaggregation is useful because the different types of. construction behave differently and because alterations and additions do not contribute to the. number of new dwellings an important factor in explaining the vacancy rate In Section 4 2 we then. discuss how these equations are mapped into dwelling investment and completions Modelling. approvals rather than dwelling investment as many other researchers do has a couple of. advantages Approvals lead dwelling investment so should improve near term forecasts The earlier. timing reduces scope for explanatory variables to react allaying endogeneity concerns It reduces. the lag from shocks to observed decisions And it facilitates distinctions between the number and. value of new dwellings a feature we exploit in modelling vacancies in Section 4 3. There are several key features of the data that the model needs to reflect First as shown in Figure 2. dwelling investment has grown at a similar rate to real income over the past 60 years with the ratio. between these variables fluctuating around a fairly stable mean Approvals data show a similar nexus. but are only available from 1973 It might seem unsurprising that construction activity grows in line. with the rest of the economy however this is not a feature of some other models for example Ball. Meen and Nygaard 2010 Table 2, As we note below the stability of the ratio in Figure 2 bottom panel means that other highly. persistent variables such as real interest rates real construction costs or real housing prices do not. have large long run effects on investment even though some theories suggest they should. Accelerator models suggest that the level of net investment should depend on the growth as well. as the level of income However we do not find the growth in income to have explanatory power. As can be seen in Figure 2 top panel the rapid mining driven growth in income in the 2000s was. accompanied by sluggish dwelling investment from a high starting point Slow growth in income. over the past decade was accompanied by a construction boom. 1 The Australian Bureau of Statistics ABS measure of building approvals ABS Cat No 8731 0 is based on local council. building permits It should not be confused with development approvals which typically occur months or even years. Figure 2 Dwelling Investment and Real Household Disposable Income2. index Level index,2015 average 100,Dwelling investment.
Real household disposable income,index Ratio index. Long run average 100,1970 1982 1994 2006 2018, Note a Before interest payments including unincorporated enterprises deflated by trimmed mean CPI from 1982 onwards and. headline CPI prior to this,Sources ABS RBA, 2 For these and other figures variable definitions and sources are given in Online Appendix D. Second in the short run construction activity seems to be highly responsive to changes in interest. rates Figure 3 top panel and changes in existing housing prices bottom panel. Figure 3 Changes in Building Approvals,and real interest rates ppt. A Model of the Australian Housing Market Trent Saunders and Peter Tulip Research Discussion Paper 2019 01 March 2019 Economic Research Department Reserve Bank of Australia We are especially grateful to Glenn Otto for detailed thoughtful comments Thanks also to Tom Cusbert Peter Downes Luci Ellis Liz Kendall Matt Larkin Dominic Meagher Ewan Rankin Dan Rees Tom Rosewall Paul Ryan John

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