آیا رابطه بین سیاست پولی و قیمت نامتقارن مسکن در بازاری بی پروا و سلف فروشی در آفریقای جنوبی است؟ مدارک و شواهد از یک مدل بردار خودبازگشت (اتورگرسیو) مارکوف سوئیچینگ
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|27896||2013||11 صفحه PDF||سفارش دهید||8572 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Economic Modelling, Volume 32, May 2013, Pages 161–171
This paper examines asymmetries in the impact of monetary policy on the middle segment of the South African housing market from 1966:M2 to 2011:M12. We use Markov-switching vector autoregressive (MS-VAR) model in which parameters change according to the phase of the housing cycle. The results suggest that monetary policy is not neutral as house price growth decreases substantially with a contractionary monetary policy. We find that the impact of monetary policy is larger in bear regime than in bull regime; indicating the role of information asymmetry in reinforcing the financial constraint of economic agents. As expected, monetary policy reaction to a positive house price shock is found to be stronger in the bull regime. This suggests that the central bank reacts more in bull regime in order to prevent potential crisis related to the subsequent bust in house prices bubbles which are more prominent in bull markets. These results substantiate important asymmetries in the dynamics of house prices in relation to monetary policy, vindicating the advantages of generating regime dependent impulse response functions.
The recent global economic downturn attributed to the sub-prime crisis in the US with rapid contagion worldwide has attracted the attention of academics and policymakers of both developed and developing countries, and South Africa is no exception. As observed during the “Great Recession”, the bursting of the house price bubble is generally followed by significant contractions in the real economy.1 Over the last two decades, South Africa has witnessed a rapid appreciation in home values which has been shown to have affected the real economy, through consumption, at both aggregate and provincial levels (Das et al., 2011, Ncube and Ndou, 2011, Peretti et al., forthcoming, Simo-Kengne et al., 2012 and Simo-Kengne et al., forthcoming).2 Furthermore, Gupta and Hartley (forthcoming) point out that house price in South Africa, is a leading indicator for output and inflation, and hence, can provide important information as to where the real economy is heading. Given this, it is crucial for central banks to analyze thoroughly the effects of monetary policy on asset prices in general and real estate in particular, which in turn, would lead to the understanding of the effects of monetary policy on the economy at large.3 Against this backdrop, the main objective of this paper is not only to analyze the impact of interest rate on South African house prices, but also, to check if the effect is asymmetric depending on whether the housing market is in a bull or bear regime. Intuitively, an increase in the interest rates tends to increase the user cost of capital which translates into a decrease in housing activity and consequently a fall in real estate prices (Demary, 2010). Furthermore, the class of models developed by Bernamke and Gertler (1989) and Kiyotaki and Moore (1997), in which there exist agency costs of financial intermediation (finance constraint) asserts that when there is information asymmetry in the financial market, agents may behave as if they were constrained financially. Moreover, the financial constraint is more likely to bind in bear markets. Hence, a monetary policy may have greater effects in bear markets. Furthermore, recent studies by Ncube and Ndou (2011), Peretti et al. (forthcoming), Simo-Kengne et al. (forthcoming), highlight that the South African Reserve Bank (SARB) has systematically reacted to house price movements.4 Given the possibility of a feedback of house prices onto the interest rate setting behavior of the SARB, we use a Markov-switching vector autoregressive (MS-VAR) model comprising the interest rate and house price, rather than the standard Markov switching regressions popularly used when analyzing the impact of monetary policy on asset returns (mainly stock returns),5 which in turn, assumes exogeneity of the monetary policy instrument.6 On one hand, the MS structure allows us to characterize the time series dynamics in different states, and on the other hand, the VAR structure allows for possible endogeneity in the relationship between monetary policy and house prices. To the best of our knowledge, the study by Chang et al. (2011) is the only other existing study that has utilized the MS-VAR approach to analyze the impact of monetary policy on housing returns (besides equity real estate investment trusts and stock returns) for the US. Though this paper does not provide a clear identification of the housing cycle in terms of bull and bear markets, the authors indicate that, following an innovation in Federal Funds rate, housing returns decline substantially more in low-volatility regime than in high-volatility regime. However, this paper did not analyze the possible feedback from housing returns to interest rate. More importantly, with no confidence intervals provided for the impulse response functions generated from the MS-VAR model, one cannot gauge whether the effects were significant or not. Though a few studies, namely, Gupta and Ndahiriwe (2010), Gupta et al. (2010) and Ncube and Ndou (2011), indicate a negative impact of monetary policy on house prices in South Africa, none of these studies investigated the possible asymmetry in this effect. Further, studies, such as Ncube and Ndou (2011), Peretti et al. (forthcoming), Simo-Kengne et al. (forthcoming), which analyze the plausibility of a feedback from housing prices onto interest rate, did not say anything about the nature of this relationship during bull and bear housing markets. The reason being that all these studies, except Peretti et al. (forthcoming), used linear (structural, factor-augmented and panel)VAR models, and hence, could not account for possible non-linearities in the relationship between interest rate and house prices that could exist under different states of the housing market. Peretti et al. (forthcoming) used a time-varying parameter VAR model, which accounted for non-linearities in the relationship between consumption, interest rate and house prices, and was able to depict the changes in the nature of this relationship over time. However, this paper, did not discuss how monetary policy reacted to house price movements during bear and bull markets, though it could have, having identified the regimes. South African housing market is categorized into luxury, middle and affordable segments based on the price of the properties, with the middle-segment being further divided into, large, medium and small based on sizes of the houses.7 In this paper, besides analyzing the entire middle-segment, we also look at the different size category of this segment, to capture possible heterogeneity in the relationship between house prices and interest rate. Given that a MS-VAR is parameter intensive, we use the maximum possible span of monthly data covering the period of 1966:1-2011:12, which is a departure from the quarterly data-based earlier studies related to house prices and interest rate in South Africa. In this regard, note that, with house price being identified as a leading indicator, Gupta (forthcoming) emphasizes that one should carry out the analysis on housing markets at the highest possible frequency. Due to this, we had to rule out the luxury and affordable sections of the housing market, since data on these two segments are only available at quarterly frequency. However, with Gupta et al. (2010), Das et al. (2011) and Inglesi-Lotz and Gupta (forthcoming) indicating that policies does not significantly affect these two extreme ends of the market, we believe, that the compromise in the form of losing information on the luxury and affordable segments by using monthly frequency, is not a serious one. As in the existing literature on housing markets and interest rate in South Africa, the monetary policy instrument is chosen to be the three months Treasury bill rate.8 Ultimately, we look at four sets of bivariate MS-VAR models9 comprising real house price of the entire, large, medium and small middle-segments considered individually, along with the three months Treasury bill rate. The rest of the paper is structured as follow: Section 2 briefly presents the Markov switching framework and discusses the estimation and identification procedures, while Section 3 describes the data used. Section 4 reports the empirical results with regard to the potential asymmetric effects of monetary policy on house prices and vice versa. Finally, Section 5 concludes.
نتیجه گیری انگلیسی
This paper investigates whether the impact of monetary policy shock on the entire middle-segment of the South African housing market, and its three categories based on sizes, is asymmetric using a MS-VAR approach spanning the period of 1966:M2 to 2011:M12. We find that monetary policy is non-neutral, as a contractionary monetary policy significantly depresses real house prices irrespective of house sizes. Furthermore, important asymmetries are found in the dynamics of house prices following a monetary policy shock in the bull and bear regimes identified for the housing market. Monetary policy is found to have larger effect on real house price inflation during the bear market-regime, thus supporting theoretical models that suggest the role of information asymmetry in reinforcing financial constraints of economic agents during this regime. This finding is robust to the aggregate and the various house sizes within the middle-segment. Given that we used a MS-VAR to account for a possible feedback effect of house price movements on monetary policy setting, we also analyzed the impact on the interest rate following a house price shock. We found evidence of positive feedbacks from housing prices to the interest rate, which, in turn, confirmed that monetary policy in South Africa reacts to house price shocks, with the SARB found to respond more to a positive house price shock in the bull regime. This is not surprising, given that a bull market is possibly associated with house price bubbles, thus leading the monetary authority to react stronger in this regime in an attempt to prevent economic recession due the subsequent bust. Also, house price increases are likely to be more inflationary in the bull-regime than the bear regime, due to a bigger impact on the aggregate demand via the wealth effect of real house price increases. Finally, while the effect of monetary policy is consistent across house sizes in general, the reaction of the central banker to a house price shock in the small sized middle segment contrasts the remaining categories. In this segment, the reaction of monetary policy is found to be stronger in the bear regime; suggesting that houses in the small-segment are more likely to be investment goods and hence, are more attractive in the bear regime given the optimism of economic agents which, in turn, motivates a stronger response from the monetary authority to prevent possible inflationary pressures. Alternatively, there is also some evidence that house price expectations are, at least to some extent, backward-looking. This is reflected by the fact that house prices show inertia and follow long cycles, plausibly displaying bubbles. Monetary authorities may, therefore, respond more in a bear market because they are concerned with credit quality and financial stability, more than by inflation prospects, which would be benign in a bear market (unless the housing market is disconnected from the rest of the economy). It is also possible that the lower segment of the market is more representative of wider economic conditions than higher segments, where buyers might be less credit constrained and where there may be more support for prices (for example because of better locations, foreign buyers). Hence a pick-up in lower segment prices may provide a stronger signal for normalizing interest rates when those are low in a bust.