News shocks, government subsidies and housing prices in Brazil

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Cássio Besarria ◽  
Marcelo Silva ◽  
Diego Jesus
Keyword(s):  
Real Estate ◽  
Interest Rates ◽  
Housing Prices ◽  
Financial Sector ◽  
Government Policies ◽  
Housing Sector ◽  
Dsge Model ◽  
News Shocks ◽  
Content Type ◽  
Real Estate Prices

Purpose In recent years, housing prices in Brazil have shown a surprising growth. An important issue is trying to understand what elements can explain this behavior. This study aims to investigate the hypothesis that a generalized optimism associated with government policies directed to the housing sector may be behind the behavior of real estate prices. This study develops a dynamic stochastic general equilibrium (DSGE) model to investigate these issues. The results showed that subsidies combined with the easing of credit conditions were able to positively influence real estate prices. Moreover, unanticipated shocks had a greater impact on housing prices than anticipated shocks. Design/methodology/approach The DSGE model was developed to analyze the relationship between economic agents’ expectations about future economic developments, also known in the literature as “news shocks,” expansionary fiscal policy and housing prices in Brazil. The economy is composed of families, entrepreneurs, final goods firms, a financial sector and a fiscal authority. Families are divided into two groups: patients or savers and impatient or debtors. They differ in terms of their intertemporal discount factors. Both provide labor for firms producing non-durable goods. Impatient families are restricted in the amount of borrowing they can take. The production side of economy model is given by the consumer goods production sector. The financial sector is composed of a representative bank that pays the deposits made by patient families and channels resources for the granting of housing loans with the accumulation of assets subject to regulatory restrictions. Findings The results show that both price subsidies and subsidized interest rates exerted a positive influence on housing prices in Brazil. In response to a housing demand shock, housing prices display a greater increase the greater are the subsidies to low income families. The authors show that anticipated shocks have a larger impact on housing prices than unexpected shocks. Therefore, the results support the idea that the wave of good news, optimistic behavior and government policies aimed at the housing sector were behind the behavior of housing prices in Brazil. Originality/value There are some studies applied to the Brazilian economy that mention some of these stimuli. In this study, the authors focused on studies proposed by Mendonça et al. (2011), Mendonça (2013), Silva et al. (2014) and Besarria et al. (2016). In general, the authors show that there is a negative relationship between monetary policy instruments and real estate prices. This paper differs from these authors by considering the effects of government subsidies, subsidized interest rates and anticipated shocks from a DSGE model, thus explicitly addressing their effects on housing prices in Brazil.

scholarly journals Liquidity, interest rates and house prices in the euro area: a DSGE analysis

2016 ◽  
Vol 9 (1) ◽  
pp. 4-25 ◽  
Author(s):  
Margarita Rubio ◽  
José A. Carrasco-Gallego
Keyword(s):  
Interest Rates ◽  
Monetary Union ◽  
Euro Area ◽  
House Prices ◽  
Housing Prices ◽  
House Price ◽  
Dsge Model ◽  
Content Type ◽  
The Core ◽  
Dynamic Stochastic

Purpose This study aims to build a two-country monetary union dynamic stochastic general equilibrium (DSGE) model with housing to assess how different shocks contributed to the increase in housing prices and credit in the European Economic and Monetary Union. One of the countries is calibrated to represent the core group in the euro area, while the other one corresponds to the periphery. Design/methodology/approach In this paper, the authors explore how a liquidity shock (or a decrease in the interest rate) affects house prices and the real economy through the asset price and the collateral channel. Then, they analyze how a house price shock in the periphery and a technology shock in the core countries are transmitted to both economies. Findings The authors find that a combination of an increase in liquidity in the euro area coming from the common monetary policy, together with asymmetric house price and technology shocks, contributed to an increase in house prices in the euro area and a stronger credit growth in the peripheral economies. Originality/value This paper represents the theoretical counterpart to empirical studies that show, through macroeconometric models, the interrelation between liquidity and other shocks with house prices. Using a DSGE model with housing, the authors disentangle the mechanisms behind these empirical findings.

Property investor decisions using income and rental ratio signals

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Billie Ann Brotman
Keyword(s):  
Real Estate ◽  
Housing Market ◽  
Discount Rate ◽  
Price Index ◽  
Housing Prices ◽  
Housing Price ◽  
Consumer Sentiment ◽  
Content Type ◽  
Income Ratio ◽  
Real Estate Prices

PurposeThis paper, a case study, aims to consider whether the income ratio and rental ratio tracks the formation of residential housing price spikes and their collapse. The ratios are measuring the risk associated with house price stability. They may signal whether a real estate investor should consider purchasing real property, continue holding it or consider selling it. The Federal Reserve Bank of Dallas (Dallas Fed) calculates and publishes income ratios for Organization for Economic Cooperation and Development countries to measure “irrational exuberance,” which is a measure of housing price risk for a given country's housing market. The USA is a member of the organization. The income ratio idea is being repurposed to act as a buy/sell signal for real estate investors.Design/methodology/approachThe income ratio calculated by the Dallas Fed and this case study's ratio were date-stamped and graphed to determine whether the 2006–2008 housing “bubble and burst” could be visually detected. An ordinary least squares regression with the data transformed into logs and a regression with structural data breaks for the years 1990 through 2019 were modeled using the independent variables income ratio, rent ratio and the University of Michigan Consumer Sentiment Index. The descriptive statistics show a gradual increase in the ratios prior to exposure to an unexpected, exogenous financial shock, which took several months to grow and collapse. The regression analysis with breaks indicates that the income ratio can predict changes in housing prices using a lead of 2 months.FindingsThe gradual increases in the ratios with predetermine limits set by the real estate investor may trigger a sell decision when a specified rate is reached for the ratios even when housing prices are still rising. The independent variables were significant, but the rent ratio had the correct sign only with the regression with time breaks model was used. The housing spike using the Dallas Fed's income ratio and this study's income ratio indicated that the housing boom and collapse occurred rapidly. The boom does not appear to be a continuous housing price increase followed by a sudden price drop when ratio analysis is used. The income ratio is significant through time, but the rental ratio and Consumer Sentiment Index are insignificant for multiple-time breaks.Research limitations/implicationsInvestors should consider the relative prices of residential housing in a neighborhood when purchasing a property coupled with income and rental ratio trends that are taking place in the local market. High relative income ratios may signal that when an unexpected adverse event occurs the housing market may enter a state of crisis. The relative housing prices to income ratio indicates there is rising housing price stability risk. Aggregate data for the country are used, whereas real estate prices are also significantly impacted by local conditions.Practical implicationsRatio trends might enable real estate investors and homeowners to determine when to sell real estate investments prior to a price collapse and preserve wealth, which would otherwise result in the loss of equity. Higher exuberance ratios should result in an increase in the discount rate, which results in lower valuations as measured by the formula net operating income dividend by the discount rate. It can also signal when to start reinvesting in real estate, because real estate prices are rising, and the ratios are relative low compared to income.Social implicationsThe graphical descriptive depictions seem to suggest that government intervention into the housing market while a spike is forming may not be possible due to the speed with which a spike forms and collapses. Expected income declines would cause the income ratios to change and signal that housing prices will start declining. Both the income and rental ratios in the US housing market have continued to increase since 2008.Originality/valueA consumer sentiment variable was added to the analysis. Prior researchers have suggested adding a consumer sentiment explanatory variable to the model. The results generated for this variable were counterintuitive. The Federal Housing Finance Agency (FHFA) price index results signaled a change during a different year than when the S&P/Case–Shiller Home Price Index is used. Many prior studies used the FHFA price index. They emphasized regulatory issues associated with changing exuberance ratio levels. This case study applies these ideas to measure relative increases in risk, which should impact the discount rate used to estimate the intrinsic value of a residential property.

Real effects of real estate: evidence from unemployment rates

2020 ◽  
Vol 37 (4) ◽  
pp. 605-623
Author(s):  
Can Dogan ◽  
John Can Topuz
Keyword(s):  
Real Estate ◽  
Housing Prices ◽  
Real Economy ◽  
Residential Real Estate ◽  
Content Type ◽  
Unemployment Rates ◽  
The Real ◽  
Real Estate Price ◽  
Real Effects ◽  
Real Estate Prices

Purpose This paper aims to investigate the relationship between residential real estate prices and unemployment rates at the Metropolitan Statistical Area (MSA) level. Design/methodology/approach This paper uses a long time-series of MSA-level quarterly data from 1990 to 2018. It uses an instrumental variable approach to estimate the effects of residential real estate prices on unemployment rates using the geography-based land constraints measure of Saiz (2010) as the instrument. Findings The results show that changes in residential real estate prices do not have a causal effect on unemployment rates in the same quarter. However, it takes 9-12 months for an increase (decrease) in real estate prices to decrease (increase) unemployment rates. This effect is significant during both pre- and post-financial crisis periods and robust to control for the economic characteristics of MSAs. Research limitations/implications This paper contributes to the emerging literature that studies the real effects of real estate. Particularly, the methodology and the findings can be used to investigate causal relationships between housing prices and small business development or economic growth. The findings are also of interest to policymakers and practitioners as they illustrate how and when real estate price shocks propagate to the real economy through unemployment rates. Practical implications This study’s findings have important implications for academics, policymakers and investors as they provide evidence of a snowball effect associated with shocks to real estate prices: increasing (decreasing) unemployment rates following a decrease (increase) in real estate prices exacerbates the real estate price movements and their economic consequences. Originality/value This paper analyzes a significantly longer period, from 1990 to 2018, than the existing literature. Additionally, it uses the MSA-level land unavailability measure of Saiz (2010) as an instrument to explore the effects of residential real estate prices on unemployment rates and when those effects are observed in the real economy.

The user cost of housing in the Baltic states

2017 ◽  
Vol 10 (1) ◽  
pp. 17-34
Author(s):  
Darius Kulikauskas
Keyword(s):  
Real Estate ◽  
Interest Rates ◽  
Housing Prices ◽  
Residential Real Estate ◽  
Baltic States ◽  
Data Set ◽  
Content Type ◽  
Real Estate Markets ◽  
User Costs ◽  
The Baltic

Purpose This paper aims to use the user costs approach to identify the periods of over- and under-valuation in the Baltic residential real estate markets. Design/methodology/approach Three alternative estimates of the user costs of homeownership in the Baltics are computed: one that does not discriminate between the leveraged and unleveraged parts of a house and the other that takes loan-to-value ratios into account. Findings The approach successfully identifies the overheating that took place in the Baltic real estate markets prior to the crisis of 2009 and shows that there is significant upward pressure for the housing prices in the Baltics in the low interest rate environment that became prevalent ever since. Research limitations/implications The paper uses only the current values of the fundamentals to compute the user costs. The framework could be augmented to account for the expected future developments of the fundamentals. Practical implications The macroprudential policy makers should monitor the developments in the Baltic residential real estate markets closely and be ready to act because an increase in the price-to-rent ratios might seem sustainable, given the current low interest rates, but could potentially bring harmful volatility when the monetary policy normalises. Originality/value This paper builds a novel data set on the real estate markets of the Baltic countries and is the first to derive the user costs of homeownership in the region. It is also among the first to identify periods of housing price misalignments from their fundamental values in the Baltic States.

scholarly journals Does immigration affect residential real estate prices? Evidence from Australia

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Narvada Gopy-Ramdhany ◽  
Boopen Seetanah
Keyword(s):  
Real Estate ◽  
Housing Prices ◽  
Past Research ◽  
Housing Demand ◽  
Interstate Migration ◽  
Causality Analysis ◽  
Residential Real Estate ◽  
Content Type ◽  
Long Run ◽  
Real Estate Prices

Purpose This study aims to investigate the effect of immigration on housing prices in Australia both at the national and regional levels. Design/methodology/approach Data for eight Australian states on a quarterly basis from 2004–2017 is used. To study the possible dynamic and endogenous relationship between housing prices and immigration, a panel vector autoregressive error correction model (PVECM) is adopted. Findings Analysis of the results indicates that in the short run immigration positively and significantly affects housing prices, whereas in the long run no significant relationship was observed between the two variables. From the regional breakdown and analysis, it is discerned that in some states there is a significant and positive effect of immigration on residential real estate prices in the long run. Causality analysis confirms that the direction of causation is from immigration to housing prices. Practical implications The study illustrates that immigration and interstate migration, as well as high salaries, have been causing a rise in housing demand and subsequently housing prices. To monitor exceedingly high housing prices, local authorities should be controlling migration and salary levels. Originality/value Past research studies had highlighted the importance of native interstate migration in explaining the nexus between immigration – housing prices. In this study, it has been empirically verified how immigration has been affecting the locational decisions of natives and subsequently how this has been affecting housing prices.

The determinants of real estate prices in a European context: a four-level analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Antonio M. Cunha ◽  
Júlio Lobão
Keyword(s):  
European Union ◽  
Real Estate ◽  
Interest Rates ◽  
Regression Models ◽  
Important Determinant ◽  
Price Change ◽  
Content Type ◽  
Real Estate Price ◽  
Price Determinants ◽  
Real Estate Prices

PurposeThis paper explores the real estate price determinants at four geographical levels: in the European Union as a whole, in the 28 European Union countries, in one European Union country (Portugal) and in 25 Portuguese metropolitan statistical areas (MSAs).Design/methodology/approachThe authors run two time series regression models and two panel data regression models with observations of potential real estate price determinants and House Price Indices collected from Eurostat.FindingsThe results show that price determinants, such as gross domestic product (GDP), interest rates, housing starts and tourism, are statistically significant, but not in all the four geographical levels of analysis. The results also confirm the autoregressive characteristic of real estate prices, with the last period price change being the most important determinant of current period real estate price change.Practical implicationsForecasting real estate prices can be made more effective by knowing that each geographical level of analysis implies different price determinants and that momentum is an important determinant in real estate returns.Originality/valueTo the best of the authors knowledge, this is the first study to develop and test a real estate price equilibrium model at several different geographical levels of the same political space.

scholarly journals South Africa’s economic response to monetary policy uncertainty

2017 ◽  
Vol 44 (2) ◽  
pp. 282-293 ◽  
Author(s):  
Mehmet Balcilar ◽  
Rangan Gupta ◽  
Charl Jooste
Keyword(s):  
Monetary Policy ◽  
Interest Rates ◽  
Stochastic Volatility ◽  
Stochastic Volatility Model ◽  
Economic Consequences ◽  
Policy Uncertainty ◽  
Dsge Model ◽  
Content Type ◽  
Volatility Model ◽  
Central Bankers

Purpose The purpose of this paper is to study the evolution of monetary policy uncertainty and its impact on the South African economy. Design/methodology/approach The authors use a sign restricted SVAR with an endogenous feedback of stochastic volatility to evaluate the sign and size of uncertainty shocks. The authors use a nonlinear DSGE model to gain deeper insights about the transmission mechanism of monetary policy uncertainty. Findings The authors show that monetary policy volatility is high and constant. Both inflation and interest rates decline in response to uncertainty. Output rebounds quickly after a contemporaneous decrease. The DSGE model shows that the size of the uncertainty shock matters – high uncertainty can lead to a severe contraction in output, inflation and interest rates. Research limitations/implications The authors model only a few variables in the SVAR – thus missing perhaps other possible channels of shock transmission. Practical implications There is a lesson for monetary policy: monetary policy uncertainty, in isolation from general macroeconomic uncertainty, often creates unintended adverse consequences and can perpetuate a weak economic environment. The tasks of central bankers are incredibly difficult. Their models project output and inflation with relatively large uncertainty based on many shocks emanating from various sources. It matters how central bankers react to these expectations and how they communicate the underlying risks associated with setting interest rates. Originality/value This is the first study that looks into monetary policy uncertainty into South Africa using a stochastic volatility model and a nonlinear DSGE model. The results should be very useful for the Central Bank as it highlights how uncertainty, that they create, can have adverse economic consequences.

What drives housing prices, rent and new construction in China

2017 ◽  
Vol 10 (5) ◽  
pp. 662-686
Author(s):  
Dimitrios Staikos ◽  
Wenjun Xue
Keyword(s):  
Real Estate ◽  
Empirical Test ◽  
Housing Prices ◽  
Panel Cointegration ◽  
Real Estate Market ◽  
Estimation Methods ◽  
Content Type ◽  
New Construction ◽  
Using Data ◽  
First Time

Purpose With this paper, the authors aim to investigate the drivers behind three of the most important aspects of the Chinese real estate market, housing prices, housing rent and new construction. At the same time, the authors perform a comprehensive empirical test of the popular 4-quadrant model by Wheaton and DiPasquale. Design/methodology/approach In this paper, the authors utilize panel cointegration estimation methods and data from 35 Chinese metropolitan areas. Findings The results indicate that the 4-quadrant model is well suited to explain the determinants of housing prices. However, the same is not true regarding housing rent and new construction suggesting a more complex theoretical framework may be required for a well-rounded explanation of real estate markets. Originality/value It is the first time that panel data are used to estimate rent and new construction for China. Also, it is the first time a comprehensive test of the Wheaton and DiPasquale 4-quadrant model is performed using data from China.

scholarly journals Asymmetric Macroeconomic Shocks and Asset Price Behaviors in Selected African Countries

2021 ◽  
Vol 11 (2) ◽  
pp. 90
Author(s):  
Saliu Mojeed Olanrewaju ◽  
Ogunleye Edward Oladipo
Keyword(s):  
Real Estate ◽  
Interest Rates ◽  
Asset Prices ◽  
Asset Price ◽  
Panel Cointegration ◽  
African Countries ◽  
Vector Autoregressive ◽  
Macroeconomic Variables ◽  
Real Estate Prices ◽  
The Relationship

This study examines the relationship between Asset prices (Stock and Real estate prices) and Macroeconomic variables in four selected African countries. The study employs the Westerlund Error Correction Based Panel Cointegration test and Eight-variable Structural Vector Autoregressive model to examine the relationship between asset prices and macroeconomic variables. Findings from the study confirm that no long-run relationship exists between both Asset prices and macroeconomic variables. The study equally reveals that portfolio diversification benefits of both stock and real estate markets are more pronounced in the period of a boom than the recession period in Africa. The results also show that GDP growth rate shock exerts a significant impact on both asset prices during expansion and recession periods. The study reveals that foreign interest rates and World oil price shocks are better predictors of both stock and real estate prices during the crisis period than in the expansion period.

Decision time for ACEES Group LLC: to buy or to lease?

2018 ◽  
Vol 14 (4) ◽  
pp. 394-426
Author(s):  
Sanjay Kudrimoti ◽  
Raminder Luther ◽  
Sanjay Jain
Keyword(s):  
Real Estate ◽  
Interest Rates ◽  
Cash Flow ◽  
Financial Management ◽  
Business Students ◽  
Primary Source ◽  
Decision Time ◽  
Business Incubator ◽  
Content Type ◽  
Real Estate Valuation

Synopsis As the move from the business incubator loomed, Abdul Khan had to decide where his business should relocate to. ACEES Group LLC, a small consulting firm, had grown from three friends working out of Abdul Khan’s house to a 20-person firm generating more than a million dollars in revenue within five years. This growth had necessitated the need for a larger and more prominent place. Although Abdul knew he did not want to renew the lease at the incubator, and he did not want to move his business too far from its current location, but the decision he had to make was whether ACEES Group should lease a commercial place or buy its own property. He was particularly torn because the real estate prices had fallen considerably, and were now on the mend and interest rates were still low. Research methodology The primary source of materials in the case was an interview with the owner (pseudo name: Abdul Khan). The owner wishes to remain anonymous. The financial statements of the firm produced in the case have been modified by a fixed factor so as to disguise the actual numbers but not materially alter the information in any fashion. Other secondary sources of materials include information about the business incubator program, the MBE certification and its benefits through the State of Florida, real estate and lease rates in Central Florida and other economic information. Relevant courses and levels This case is primarily intended for undergraduate students taking a course in entrepreneurship, real estate investments or financial management, with emphasis on real estate valuation, cash flow forecasting and/or valuation of business. Students should be familiar with time value of money concepts, understand the concept of NPV and IRR, and preferably be comfortable in the use of Excel. This instructor manual provides all calculations of space needs analysis, and discounted cash flow analysis for lease vs buy analysis. A few suggestions to discuss qualitative aspects of this decision making are also included.

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