Housing Market

In this paper, we add specialist investors to a canonical housing market search model and study how investors participation affects price, turnover, vacancy, trans- action volume, and welfare. In our model, the central role played by investors is to facilitate the turnover of mismatched houses on behalf of households. A crucial as- sumption is that ordinary households cannot hold more than one house at a time. The assumption, of course, can be justified by the usual liquidity constraint argument. In this case, a household which desires to move because the old house is no longer a good match must first sell it before the household can buy up a new house. In the usual housing market search model, the household must wait out a buyer who finds the old house a good match to arrive, which can be a lengthy process, especially in a buyer’s market, one in which sellers outnumber buyers by a significant margin. This opens up profitable opportunities for specialist investors to just buy up the mismatched house at a discount in return for the time spent waiting for the eventual end-user buyer to arrive on behalf of the original owner. Meanwhile, transaction volume, vacancy, and housing price all increase with the extent of investors participation, whereas average Time-On-the-Market (TOM) declines in the interim.

In this section we present a simple demand model. While the housing market consists of new construction and sales from the existing stock, it is well known that only the supply of new construction can adjust to a credit fuelled housing demand boom in the short and medium run (Augustyniak et al. 2014). The supply from the existing housing stock is rigid in the short and medium run, thus any excessive demand translates very quickly into excessive demand for new construction. Therefore, we focus on the primary market only and we assume that households finance home purchase with a loan. The cost incurred in a particular period by the household is the loan repayment. Burnham (1972) quotes a Fed survey, according to which credit supply determines housing construction. Currently, we see that housing demand both in Poland and across the world is driven by credit supply (see NBP, 2012a,b). Moreover, demand is affected by consumer preferences as regards the consumption of other goods C and housing services. Like Bajari et al. (2013) we include the imputed rent in the utility function. It results from the size of the apartment H, its price p and the parameter k, which reflects the monetary value of the stream of housing services. The utility is described by a CES function, where θ is the share of utility resulting from consumption, whereas the parameter μ denotes the elasticity of substitution between consumption and housing, ε = 1 / (1-μ). Accounting for appreciation,

This dissertation investigates the investment strategies of major private property firms in Hong Kong in relation to reducing risk and understanding the housing market factors. Risk is taken to be the measurement of a potential loss, identified as a possible outcome of the decision (Byrne and Cadman, 1984). Risk lies at the root of property development which would rank as one of the most speculative activities, involving relatively large amounts of capital into a product that is fixed both in time and space. Some methods have already been invented to estimate and tackle the risk for the property developers, such as residual valuation, sensitivity analysis, and diversification. However, all these methods require variables which are based on the decision-maker’s assumptions. Thus, it is crucial for developers to form their own judgment about the variable factors and risks, based on the local housing market content, so that they are able to give more accurate assumptions to make these methods work.

distinguish these two (sub-)markets by the subscript i = { } R, S , where R = rental market and S = homeownership or sale market. Hence, p R is the rental price and p S is the selling price. There are two main categories of home seekers in this housing market matching model: people who search for a dwelling both in the rental and in the homeownership market, simply named “seekers” ( h ), and people who pay a rent, thus searching only in the homeownership market, named “tenants” ( h S ). We assume that the mass of households/persons who need to change their home (for business reasons or family needs) increases over time and all the “new” home-seekers λ (where λ is a positive and exogenous number) initially search on both markets, i.e. they enter the seekers pool ( h ). As regards the supply side, i.e. the housing offer, there is free entry into the market. Hence, it is the free entry condition which allows the equilibrium value of vacant houses to be determined. In short, new vacant houses will be posted until the value of a further vacancy becomes equal to zero. In equilibrium, in fact, all the profit opportunities derived from opening new vacancies have been exploited, therefore the value of an additional vacancy is equal to zero (see Pissarides, 2000). 4 Precisely, in this model, sellers post vacancies in the homeownership market and landlords open vacancies in the rental market. 5 Hence, landlords only meet with the seekers ( h ).

Housing rents have been observed to display some interesting behaviours in the housing markets. First, Glaeser and Gyourko (2007) find that, not only housing prices but also rents help to predict future returns on houses. 1 Second, Sinai and Souleles (2005) find that, the larger the rent volatility in a given market, the higher the housing price in that market. While Sinai and Souleles (2005) have provided a risk-hedging explanation for their finding, the return predictability problem still seems unresolved; as Glaeser and Gyourko (2007) observe, “rents may add predic- tive power to housing price change regressions even if we are not sure why they have this predictive power.” From the efficient market point of view, neither hous- ing prices nor rental prices should help to predict future return on houses. Both housing prices and rental prices are publicly available, easily observed by anyone, and if they can really predict future housing prices and thereby future returns, all rational investors will take advantage of them. However, if that were the case, the competition would drive out any predictable movements in housing prices. Nonethe- less, empirical research on the housing market has consistently found evidence that suggests the predictive power of these prices. For example, Case and Shiller (1989, 1990) find that price-rent ratio has the predictive power for future returns. 2 Glaeser

Many of the concerns about the Canadian housing market are motivated by recent US experiences. Over the years 2000 to 2006, US prices appreciated nearly twice as much as Canadian houses (Figure 1 plots the United States’ S&P Case-Shiller 20 city composite index and the Canadian Teranet-National Bank 6 city composite index). 2 This rapid appreciation has been followed by an equally rapid decline, as US house prices declined by over 30 percent between 2006 and 2009, before staging a modest recovery late in 2009, only to fall into the doldrums again this year.

In this context, the duration analysis emerges as an important tool at providing some light to the issue. Having been employed in labour economics to assess the duration of spells of unemployment (Allison, 1982) and, more recently, to analyze the duration of the business cycles phases (Zuehlke, 2003; Castro, 2010, 2013) and the duration of …scal consolidation programmes (Agnello et al., 2013), this framework can be extremely useful at assessing the duration of housing market cycles. 1 The seminal work of Zuehlke (1987) paved the ground for further development in this area. The author uses a Weibull hazard model to analyze the link between the probability of sale and the market duration in housing markets. He shows that while vacant houses display positive duration dependence, such evidence is not found for occupied houses. This result is in line with the existence of stronger incentives for adopting diminishing reservation prices (or, putting it di¤erently, a higher opportunity cost) in the case of the owners of a vacant house, thereby, implying that vacant houses have a higher rate of time dependence than occupied houses. Payne and Zuehlke (2006) apply hazard models to test for duration dependence in the market for real estate investment trusts. By focusing on information about the length of time between turning points of the cycle, the authors …nd evidence of duration dependence. As a result, hazard models provide a relatively robust way of predicting the timing of mean reversion of the housing market indices.

In this formulation, R ijt is the home’s sales price commanded by dwelling i in cluster j in quarter t; X i is the set of hedonic characteristics of building i, and ε ijt is an error term. To control more precisely for locational effects, we include a set of dummy variables, one for each of the j zip codes. These zip-code-fixed effects account for cross-area differences in local public goods such as weather, crime, neighborhood demographics and school quality. To capture the time-variance in local price dynamics, we interact zip-code- fixed effects with year/month indicators; the transaction prices of homes are thus allowed to vary by each month during the time period, in each specific location. This rich set of fixed effects allows for local housing market trends and captures the value of time-varying local public goods, such as crime dynamics or the growth or decline of a nearby employment district. green i is a dummy variable with a value of one if dwelling i is rated by the EPA, USGBC or Build It Green, and zero otherwise. α, β, γ jt are estimated coefficients. α is thus the average premium, in percent, estimated for a labeled building relative to those observationally similar buildings in its geographic cluster—the zip code. Standard errors are clustered at the zip code level to control for spatial autocorrelation in prices within zip codes.

A simple model of housing market flippers as middlemen is also in Bayer et al. (2011). The model though is partial equilibrium in nature and cannot be used to answer many of the questions we ask in this paper. Intermediaries who buy up mis- matched houses from households and then sell them on their behalf are also present in the model of the interaction of the frictional housing and labor markets of Head and Lloyd-Ellis (2012). But there the assumption is merely a simplifying assumption and the presence of these agents in the given setting appears inconsequential. An- alyzes of how middlemen may serve to improve liquidity in a frictional market also include Gavazza (2012) and Lagos et al. (2011). These studies do not allow end-user households a choice of whether to deal with the middlemen and for the multiplicity of equilibrium like we do though. Multiple equilibria in a search and matching model with middlemen can also exist in Watanabe (2010). The multiplicity in that model is due to the assumption that the intermediation technology is subject to increasing returns to scale, whereas the multiplicity in ours arises from a particular strategic complementary. Moreover, only one of the two steady-state equilibria in that model is stable, whereas there can be more than one stable steady-state equilibria in ours.

Empirical studies of the housing market distinguish two main types of drivers: macroeconomic and micro geographical drivers. Recently, an increasing number of studies have examined the macro determinants of house price movements over time by aggregated data across countries (e.g. real income, real GDP, tax rates, interest rates, population, construction costs and consumer price-index). For example, Apergis and Rezetis (2003) examined the dynamic effects of macroeconomic factors on housing price, and he found that mortgage rate had the highest explanatory power in the variation of housing price, and inflation and employment had the second and third highest power. Kholdy and Sohrabian (1998) attempted to explain housing cyclesfrom1986 to 1994 in California. The results of the study suggest that speculative behavior and expectations of capital gain played a stronger role than economic fundamentals to explain housing cycles. Stern (1992) examined the determinants of house price inflation in UK for the period 1971to 1989. He found that real income was the most important determinant of house price inflation; housing supply had a somewhat greater effect on prices than had interest rates. Stevenson (2004) presented strong evidence that housing and inflation are cointegrated in the long run by utilizing cointegration tests. Conventional ordinary least squares (OLS) models provided less conclusive results. Jud and Winkler (2002) also showed that changes in real income, construction costs and interest rates, as well as growth of population are significant factors for real U.S. housing price appreciation.

The large proportion of credit secured against properties and mortgage debt expenditure are both closely correlated with short-term interest rates, especially because variable rate contracts are prevalent within the mortgage market (Koblyakova et al., 2014). As a result, Aron and Muellbauer (2010) explain that changes to mortgage policy rates pose a threat to financial stability. In the period October 2012 to September 2015, there were six consecutive cuts in cash base rates by the Reserve Bank of Australia (RBA), prompting concerns that the Australian housing market might be highly vulnerable to the transmission of monetary policy changes on a cash basis because the Australian market has an outstanding stock of residential mortgage debt to gross domestic product (GDP) ratio of 78% (ABS and RBA, 2016). Thus, this figure implies that changes in mortgage interest rate would be expected to have a significant impact on Australia’s economic output and stability of the financial system.

We also refer to the analysis of house prices carried out by Weiner and Fuerst (2015) for the Israeli market. Weiner and Fuerst (op. cit.) concentrate on the period 1998:Q1-2013:Q4, and apply cointegration techniques to model house prices. In par- ticular, their analysis focuses on exploring the role of the following set of fundamen- tals: rents, unemployment, stock prices, market volatility, mortgage rates, short-term interest rates, housing inventory-to-population and average real wage per capita. Fo- cusing on the long run, their analysis considers that shortages of supply for housing and population growth are the main explanatory forces of rising prices in the market. Other factors such as mortgage rates and unemployment also reinforce this trend, while in the short-run cycles are driven by expectations of future price increases. This contribution also reports a “substitution” effect between the stock and housing markets. More specifically, Weiner and Fuerst (2015) reports that over the period 2007:Q2-2013:Q4 rental prices have increased by 19%, while house prices rose by 62% in real terms. Drawing attention to the period 2004-2007, this contribution does not find that the acceleration of economic activity that took place had a reflection in the housing market since a decline in house prices was registered. Weiner and Fuerst (2015) also conclude that house prices in Israel was 20% above their fundamentals over the period 2009-2013.

7 istered marriage raised by 54 percent since 2007 (Beijing Statistical Yearbook). For many Chinese, especially for young couples, renting an apartment is not very popular. The regulation system does not give tenants much protection. For example, renters will lose their apartments if the home owner decides of a different use. The preference to own a house has considerably driven up the demand in the housing market. Due to high economic growth, millions of Chinese join the middle class each year, thereby contri- buting to high housing demand. Because of high saving rates, many households are able to buy a house with cash and are rather independent on mortgage loans. In addition, the uneven regional development has enhanced the housing demand in the first-tier cities, as there are better living conditions, more job opportunities, and better social and public resources.

Abstract : The housing market exhibits a puzzling yet repetitive seasonal boom and bust cycle where prices and trade volume rise in summers and fall in winters. This paper presents a search model that analytically generates the observed deterministic cycle. Keywords: housing, search, thin and thick markets, seasonality

evidence of loss aversion in the context of the Boston condominium market between 1990 and 1997. The authors report significant effects of loss aversion on listing prices and time on the market and no significant effects on transacted prices. They find a small role for down-payment effects. Relatedly, Anenberg (2011) analyzes the San Francisco Bay Area housing market and in contrast to Genesove and Mayer (2001), reports significant effects of loss aversion on transacted prices. Unlike these two studies, we find that loss aver- sion played a muted role in the England and Wales’ housing markets, not least because the overall gains in values for most properties were positive during the period analyzed. Moreover, for properties that registered losses, there is no evidence of asymmetric effects on prices or selling probabilities vis-` a-vis gains. Also differently from these studies, we in- vestigate the quantitative implications of history dependence and its underlying channels on the aggregate volume transactions.

The City of Erie has prepared a Housing Market Analysis for the City neighborhoods participating in the Neighborhood Stabilization Program (NSP). NSP was established for the purpose of stabilizing communities that have suffered from foreclosures and abandonment. Through the purchase and redevelopment of foreclosed and abandoned homes and residential properties, the goal of the program is being realized. NSP1, a term that references the NSP funds authorized under Division B, Title III of the Housing and Economic Recovery Act (HERA) of 2008, provides grants to all states and selected local governments on a formula basis. The City of Erie was awarded $2,089,416 in NSP1 funds from the PA Department of Community and Economic Development (PA DCED) which was a subrecipient of NSP 1 funds from HUD.

The parameter controls the stock of residential housing relative to annual output: setting = 0:25 …xes the ratio at around 400 percent, in line with the data from the Spanish housing market (see Appendix A for data sources and description). A consideration particular to Spain is the issue of foreign ownership in the housing market. If the share of foreigner-owned houses is high, then …xing the housing stock to GDP ratio at 400 percent can overstate the importance of housing price transmission mechanism in our model. While the Bank of Spain does not o¤er the breakdown of housing stock between local residents and foreigners, it reports the share of foreign investment in property as a percentage of GDP, which stands at only one sixteenth of total investment in residential housing. However, since is an important parameter of the model, below we perform robustness check by setting = 0:1, a level consistent with the U.S. housing market indicators.

It can be argued that policy interventions aimed at pricing levels are often inappropriate. The arguments against pricing controls are widely made in terms of rent/price control as debated during the market upswing. It was suggested that such restrictions would lead to a curbing of the supply process. In reverse the same debate now emerges related to market support measures including NAMA and other measures which have the intended or unintended effect of supporting existing price levels. The reasonable conclusion is that such measures will assist holders of existing stock but fail to address more fundamental issues which would improve long term market efficiency. A more reasonable case can be made for the position that a reduction in the confusion of conflicting policy positions and overlapping initiatives aimed at house pricing issues and affordability is often advisable. Poorly resourced and ad-hoc initiatives can prove at best inefficient for the operation of the housing market and at worst have unintended and wasteful impacts given the scarce resources available. A review of the key demand features is necessary in 2010 in light of major economic changes over the recent period. This includes the major downturn in economic conditions and the crisis in the financial sector. The economic cycle has moved sharply downwards and negative income, employment and population trends are expected over the next two years. With negative sentiment/little confidence/fear and limited credit availability house prices and supply levels have reduced significantly. This is in line with expectations that demand for property is a derived demand linked strongly with real economic growth and decline. In particular critical indicators show 2009 commencements are expected to be down 53% and 2009 completions estimated at under 26,000 with the estimates of this research on supply for 2010 and 2011 under 20,000 per annum. The value of loans paid out in 2009 was down 56% on 2008 figures and the most recent 2009 figures indicate credit growth at its lowest level since the beginning of 1994 (Tables 1 and 2). The overall decline in the value of loans paid out as the property market went into decline over 2007 and 2008 is clear in Table 1.

The last decade has been marked by cycles of excessive boom and bust in the housing market. However, not all countries have experienced high volatility in their house prices. Indeed, Germany has been unique in retaining flat price levels over the whole period and failing to respond to any of the macroeconomic shocks. The main reason for this stability can be found in real estate finance and in the existence of a sophisticated rental market. While in other countries monetary stimuli are effectively transmitted to the real economy via the housing market, the German insistence on prudential lending isolates the housing market from financial market distortions. By demanding high deposits, aligning lending to the mortgage lending value instead of the market value and by offering predominantly fixed-rate mortgages, banks reduce the risk of defaults and thus contribute to stability in the market. This system has evolved as a result not of regulations but of a sophisticated rental market which enables households to save their own funds for house purchases. This, in turn, explains the preference for fixed-rate mortgages.

We present a search-and-matching model of the housing market where potential buyers’willingness to pay is private information and sellers may become desperate as they are unable to sell. A unique steady state equilibrium exists where desperate sellers o¤er sizeable price cuts and sell faster. If the number of distressed sales rises then even relaxed sellers are forced to lower their prices. Buyers, on the other hand, become more selective and search longer for better deals. The model yields a theoretical density function of the time-to-sale, which is positively skewed and may be hump-shaped. These results are consistent with recent empirical …ndings.