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The impacts of fiscal policy shocks on the US housing market

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Abstract

We explore empirically the impact of fiscal policy shocks on the US housing market using a vector autoregressive model. Identification is achieved through sign restrictions. Accounting for announcement effects, a revenue shock has a short-lived positive impact on house prices and indicators of housing activity. The impact of a spending shock on housing activity is negative and more persistent, but there is no substantial response from house prices. A balanced budget spending expansion (i.e. 1 % increases in spending and revenue) has a short-lived negative impact on housing activity and a very persistent negative impact on house prices. The paper presents results from other combinations of the two shocks. Results are generally robust to only using data for the post-financial liberalization period (i.e. since 1983).

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Notes

  1. It is important to note that different types of housing subsidies and property taxes may also affect the housing market and may not be captured by our approach.

  2. Afonso and Sousa (2012) used a different ordering to identify each of the fiscal policy shocks. The difference is the order of the revenue and spending shocks. Our results do not change if we switch the order of these two variables.

  3. For comparison purposes, all estimations in the paper contain the full set of ten variables used in the sign restriction estimations. In their paper, Khan and Reza (2013) estimate the VAR with only five variables and do not include housing starts.

  4. The term “investment” refers to new units, improvements to existing ones, mobile homes, brokers’ commissions and net purchases of government structures.

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Correspondence to Carlos Vargas-Silva.

Appendices

Appendix 1: Data

All data are at quarterly frequency, cover the period 1963:Q1–2011:Q4 and are seasonally adjusted. All variables, with the exception of the federal funds rate are included in logs in the estimation.

GDP this is real GDP per capita. Source: Bureau of Economic Analysis (BEA). Adjusted using population measures (Census bureau) and Deflator.

Consumption this is real private consumption per capita. Source: BEA. Adjusted using population measures (Census bureau) and Deflator.

Spending this is real government expenditures per capita, defined as government consumption expenditures and gross investment. Source: BEA. Adjusted using population measures (Census Bureau) and deflator.

Revenue this is real government revenue per capita, defined as government current receipts. Source: BEA. Adjusted using population measures (Census Bureau) and deflator.

Investment this is real non-residential investment per capita. Source: BEA. Adjusted using population measures (Census Bureau) and deflator.

FFR this is effective federal funds rate. Source: Board of Governors of the Federal Reserve System. Original data were at monthly frequency, and three month averages were used as the quarterly equivalent.

Reserves this is real adjusted reserves, defined as St. Louis Adjusted Reserves. Source: Federal Reserve Bank of St. Louis. Adjusted using deflator. Original data were at monthly frequency, and three month averages were used as the quarterly equivalent.

Deflator this is gross domestic product: implicit price deflator. Source: BEA.

House prices two alternative measures.

  • Average price of houses sold, this is average sales price of houses sold for the USA. Source: Census Bureau.

  • Median price of houses sold, this is median sales price for new houses sold in the USA. Source: Census Bureau. Original data were at monthly frequency, and three month averages were used as the quarterly equivalent.

Housing activity five alternative measures.

  • Housing starts, this is new privately owned housing units Started. Source: Census Bureau. Original data were at monthly frequency, and three month averages were used as the quarterly equivalent.

  • Building permits, this is new private housing units authorized by building permits. Source: Census Bureau. Original data were at monthly frequency, and three month averages were used as the quarterly equivalent.

  • Houses sold, this is new one family houses sold. Source: Census Bureau. Original data were at monthly frequency, and three month averages were used as the quarterly equivalent.

  • Residential investment, this is private residential fixed investment. Source BEA. Adjusted using population measures (Census Bureau) and deflator.

  • Monthly supply of homes, this is the ratio of houses for sale to houses sold. Source: Census Bureau. Original data were at monthly frequency, and three month averages were used as the quarterly equivalent.

Appendix 2: Methodology

The main analysis in this paper uses sign restrictions for identification purposes. The methodology is relatively simple to explain. Equation (1) presents a VAR representation:

$$\begin{aligned} Y_t =C+A_1 Y_{t-1} +A_2 Y_{t-2} +A_3 Y_{t-3} +\cdots +A_p Y_{t-p} +e_t \end{aligned}$$
(1)

where \(Y_t \) is a \(k\, x\, 1\) vector, \(C\) is a \(k\times 1\) vector of constants, \(A_{i}\) are the \(k\, x \,k\) coefficient matrices and \(e_{t}\) is the one-step ahead prediction error. Let \(\Omega \) be the variance-covariance matrix of \(e_{t}\), that is:

$$\begin{aligned} \Omega =E[e_t e_t^{\prime } ] \end{aligned}$$
(2)

It is possible to write \(e_{t}\) as a linear combination of orthogonalized structural shocks:

$$\begin{aligned} e_t =B\varepsilon _t , \quad \hbox {with}\, E[\varepsilon _t \varepsilon _t^{\prime } ]=I \end{aligned}$$
(3)

where \(\varepsilon _{t}\) is the vector of fundamental innovations that relates to \(e_{t}\) via matrix \(B\). Researchers have proposed different identification methods to decompose the prediction error into economically meaningful fundamental innovations. The traditional strategies (i.e. Choleski, structural) impose a recursive ordering or structural restrictions.

Uhlig (2005) defines an impulse vector \(b\), in which there is some matrix \(B\), so that \(BB^{{\prime }}=\Omega \) and so that \(b\) is a column vector of \(B\). It is possible to show that the impulse response for \(b\) at some horizon \(l\) is:

$$\begin{aligned} r_b (l)=\sum _{j=1}^k {\alpha _j r_j (l)} \end{aligned}$$
(4)

where \(r_j (l)\) is the vector response at horizon l and \(\alpha \) is a \(k\)-dimensional vector. Uhlig (2005) defines a monetary policy impulse vector as one in which the impulse responses have the desired sign for the restrictions period. Following Mountford and Uhlig (2009), the method of Uhlig (2005) is extended to identify three fundamental shocks. Identifying more than one shock implies that each shock must be restricted to be orthogonal to the other structural shocks. The identification of the inequality restrictions is achieved by minimizing several criterion functions in sequence (one for the business cycle shock, one for the monetary policy shock, one for the fiscal policy shock) using a simplex algorithm. The criterion function penalizes violations of the relevant sign restrictions for the selected period.

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Ruiz, I., Vargas-Silva, C. The impacts of fiscal policy shocks on the US housing market. Empir Econ 50, 777–800 (2016). https://doi.org/10.1007/s00181-015-0961-8

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