Analysis of demand and supply in the Colombian housing market: impacts and influences 2005-2016

2018 ◽  
Vol 11 (1) ◽  
pp. 149-172 ◽  
Author(s):  
Camilo Vargas Walteros ◽  
Amalia Novoa Hoyos ◽  
Albert Dario Arias Ardila ◽  
Arnold Steven Peña Ballesteros
Keyword(s):  
Real Estate ◽  
Housing Market ◽  
Least Squares ◽  
Supply Side ◽  
Retail Sector ◽  
Demand And Supply ◽  
Content Type ◽  
Elastic Effect ◽  
Real Estate Valuation ◽  
Mortgage Rates

Purpose The purpose of this paper is to provide an estimate of the demand and supply in the housing market in Colombia in a period of high real estate valuation (2005-2016). On the demand side, it evaluates the impact of new housing prices, unemployment, stock market returns, real wages in the retail sector, remittances and mortgage rates. On the supply side, it estimates the influence of the price of new housing, construction costs, time deposit (TD) and mortgage rates. Real estate valuation was analyzed considering foreigners migration and land prices evolution. Design/methodology/approach Ordinary least squares (OLS) was used to estimate housing area with the semilog regression model and also to construct price models. OLS was also used in price models. Since quantities depend on prices and vice versa, a two-stage least squares (2SLS) was implemented. Findings Rising prices in new homes have an “elastic” effect on both demand and even higher effect on supply. Likewise, the real wage index for the retail sector has an elastic effect. On the other hand, the response to interest rates is negative, but statistically significant only on the supply side. Furthermore, the inflow of remittances is “inelastic” and statistically insignificant. Originality/value Housing can sometimes be a Giffen good, this result challenges the traditional neoclassical model, but it can be explained by investment reasons and “bubble” behavior in the housing market. One last influence is the difference between “temporary” and “permanent” migrations. The latter has a statistically significant and perfectly inelastic effect on the price of new homes.

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.

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.

The housing demand analysis and prediction of the real estate based on the AWGM (1, N) model

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xin Xiong ◽  
Huan Guo ◽  
Xi Hu
Keyword(s):  
Real Estate ◽  
Housing Market ◽  
Housing Demand ◽  
Grey Model ◽  
Content Type ◽  
First Order ◽  
Model Based ◽  
Per Capita ◽  
Improved Model ◽  
Grey Relational

PurposeThe purpose of this paper is to seek to drive the modernization of the entire national economy and maintain in the long-term stability of the whole society; this paper proposes an improved model based on the first-order multivariable grey model [GM (1, N) model] for predicting the housing demand and solving the housing demand problem.Design/methodology/approachThis paper proposes an improved model based on the first-order multivariable grey model [GM (1, N) model] for predicting the housing demand and solving the housing demand problem. First, a novel variable SW evaluation algorithm is proposed based on the sensitivity analysis, and then the grey relational analysis (GRA) algorithm is utilized to select influencing factors of the commodity housing market. Finally, the AWGM (1, N) model is established to predict the housing demand.FindingsThis paper selects seven factors to predict the housing demand and find out the order of grey relational ranked from large to small: the completed area of the commodity housing> the per capita housing area> the one-year lending rate> the nonagricultural population > GDP > average price of the commodity housing > per capita disposable income.Practical implicationsThe model constructed in the paper can be effectively applied to the analysis and prediction of Chinese real estate market scientifically and reasonably.Originality/valueThe factors of the commodity housing market in Wuhan are considered as an example to analyze the sales area of the commodity housing from 2015 to 2017 and predict its trend from 2018 to 2019. The comparison between demand for the commodity housing actual value and one for model predicted value is capability to verify the effectiveness of the authors’ proposed algorithm.

Capitalisation rates for commercial real estate investment decisions

2015 ◽  
Vol 33 (3) ◽  
pp. 242-255 ◽  
Author(s):  
John McDonald
Keyword(s):  
Real Estate ◽  
Commercial Real Estate ◽  
Office Buildings ◽  
Office Building ◽  
Vacancy Rate ◽  
Recent Past ◽  
Content Type ◽  
Empirical Results ◽  
Real Estate Valuation ◽  
Capitalization Rate

Purpose – The purpose of this paper is to present a basic model of commercial real estate valuation in which the capitalization rate is the critical variable, and to present empirical results for a study of office building capitalization rates. Design/methodology/approach – The model is derived from standard economic and financial theories. The empirical study uses data from the sale of office buildings in 37 downtown markets for 2012. The empirical results are related to concepts of asset market efficiency. Findings – The empirical results show that capitalization rates depend on features of the office buildings, vacancy rate, and recent change in the office building market as captured by the vacancy rate. In other words, investors are using variables implied by standard economic and financial theory and basic economic data from the recent past to determine the capitalization rate. Practical implications – The empirical results show how investors determine capitalization rates for office buildings, so potential investors can gauge the state of a property market. Originality/value – The paper shows that changes in capitalization rates are predictable; investors use past data to adjust their capitalization rates. Furthermore, if an investor does not agree that current trends will continue, then the investment decision should be determined accordingly. For example, if an investor thinks that the future will not be as robust as the recent past, then other investors will bid more than the investor thinks is reasonable. However, if the investor sees a future that is brighter than the recent past, it is time to buy.

Boosted regression trees

2014 ◽  
Vol 19 (2) ◽  
pp. 152-167 ◽  
Author(s):  
William J. McCluskey ◽  
Dzurllkanian Zulkarnain Daud ◽  
Norhaya Kamarudin
Keyword(s):  
Real Estate ◽  
Regression Trees ◽  
Heterogeneous Data ◽  
Boosted Regression Trees ◽  
Categorical Variables ◽  
Percentage Error ◽  
Residential Property ◽  
Data Set ◽  
Content Type ◽  
Real Estate Valuation

Purpose – The purpose of this paper is to apply boosted regression trees (BRT) to a heterogeneous data set of residential property drawn from a jurisdiction in Malaysia, with the objective to evaluate its application within the mass appraisal environment in Malaysia. Machine learning (ML) techniques have been applied to real estate mass appraisal with varying degrees of success. Design/methodology/approach – To evaluate the performance of the BRT model two multiple regression analysis (MRA) models have been specified (linear and non-linear). One of the weaknesses of traditional regression is the need to a priori specify the functional form of the model and to ensure that all non-linearities have been accounted for. For a BRT model the algorithm does not require any predetermined model or variable transformations, making the process much simpler. Findings – The results show that the BRT model outperformed the MRA-specified models in terms of the coefficient of dispersion and mean absolute percentage error. While the results are encouraging, BRT models still lack transparency and suffer from the inability to translate variable importance into quantifiable variable effects. Practical implications – This paper presents a useful alternative modelling technique, BRT, for use within the mass appraisal environment in Malaysia. Its advantages include less intensive data cleansing, no requirement to specify the predictive underlying model, ability to utilise categorical variables without the need to transform them and not as data hungry, as for example, MRA. Originality/value – This paper adds to the knowledge in this area by applying a relatively new ML model, BRT to residential property data from a jurisdiction in Malaysia. BRT has shown promise as a strong predictive model when applied in other disciplines; therefore this research empirically tests this finding within real estate valuation.

Germany will face long-term housing problems

2018 ◽  
Keyword(s):  
Young People ◽  
Real Estate ◽  
Housing Market ◽  
Social Housing ◽  
Large Scale ◽  
Content Type ◽  
Housing Investment ◽  
Will Force ◽  
German Cities

Subject Germany's housing dilemma. Significance Demographic changes and the shift from publicly to corporately owned housing have led to accommodation shortages and rising rents in West German cities. Despite the government's plans for large-scale social housing investment, these trends will have long-term implications for Germany's housing market. Impacts Over-priced real estate will force developers to invest overseas. Accommodation shortfalls and rising prices will result in mass mobilisation and protests of young people across Germany. Lack of social housing will increase rates of homelessness in German cities.

Chile retail will struggle as COVID-19 hastens change

2020 ◽  
Keyword(s):  
Real Estate ◽  
Business Model ◽  
Domestic Demand ◽  
Retail Sector ◽  
Shopping Malls ◽  
Social Unrest ◽  
Content Type ◽  
The Social ◽  
Consumer Habits ◽  
Real Estate Assets

Significance Chile’s retail sector is reeling from the successive impacts of the social unrest that erupted in late 2019 and, since March, the COVID-19 pandemic, which has also affected the operations of its larger players in other countries. Even before these two events, the industry was struggling to adapt its business model to changing consumer habits. Impacts Even if domestic demand increases by 7.7% in 2021, as the Central Bank anticipates, it will remain well below its level in mid-2019. Retail is among the sectors where most jobs have been lost during the pandemic, affecting women in particular. Shopping malls may emerge as especially vulnerable to a likely reduction in the return on retail real estate assets.

Examining why developers encourage foreign investment in local residential real estate in India

2016 ◽  
Vol 9 (4) ◽  
pp. 580-600
Author(s):  
Alok Tiwari ◽  
Mohammed Aljoufie
Keyword(s):  
Real Estate ◽  
Housing Market ◽  
United Arab Emirates ◽  
Foreign Investment ◽  
Large Population ◽  
Political Stability ◽  
Primary Data ◽  
Residential Real Estate ◽  
Content Type ◽  
Property Developers

Purpose The study aims to explore the role of non-resident Indian (NRI) investors into staggering local housing market and the efforts of developers and regulators to lure such investors. Design/methodology/approach Primary data for this exploratory study were assembled through a Google form-based questionnaire circulated over internet among NRIs residing in Kingdom of Saudi Arabia, USA, Singapore and United Arab Emirates, whereas the secondary data sources include the Government of India policy documents, World Bank data, Reserve Bank of India archives and reports published in reputed financial and others print media sources. Findings Indian housing market is confronted with a demand and supply mismatch at present. While a massive demand lingers at affordable housing segment, on the contrary, millions of housing inventories are also piling up. Consequently, property developers are attempting to lure the large population of NRIs residing at global cities. Study observes that sentimental attachment to the homeland, higher rate of returns, anticipated rental incomes are the major decisive elements. Additionally, growth in infrastructure, world-class amenities offered by developers, conformity to sustainability and political stability is the other critical reasons. Research limitations/implications On first hand, the study outlines a novel kind of foreign investment in Indian local residential real estate that is via NRI channel. Second, non-resident investors might surprise to the property developers and government through a realistic strategic approach. Originality/value Probably, the study is first of its type gazing at NRI investors, as a foreign investor, in the local residential real estate.

scholarly journals INVESTIGATING SELECTED GASTRONOMIC TRENDS FROM DEMAND AND SUPPLY SIDE

2020 ◽  
Vol 3 (1) ◽  
pp. 27-33
Author(s):  
Alžbeta Kiráľová ◽  
Lukáš Malec
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Stability Property ◽  
Supply Side ◽  
Economic Interest ◽  
Slow Food ◽  
Demand And Supply ◽  
Linear Discriminant ◽  
Quantitative And Qualitative Research

The study aims to identify the role of the selected gastronomic trends in the Czech gastronomic establishments. The study highlights the key findings of quantitative and qualitative research provided with the focus on both the demand and the supply side. It is focusing on the dispute between guests’ opinions and entrepreneurs’ views based on few variables for gastronomic trends. Entrepreneurs’ and guests’ views in three Czech Regions were studied in one set with notes incorporated on possible mutual differences between them. The partial least squares variant of linear discriminant analysis (plsLDA) and partial least squares (PLS) was applied as they give a clear superiority due to both, interpretational and stability property. It was proven that the partial least squares variants lead to direct answers to questions in the studied field. Participation/organization of food festivals and slow food are positively related. The significant tasks emerge to a great extent covering differences between guests´ and entrepreneurs´ opinions. On the other hand, the connection of economic interest to gastronomic trends is relatively weak.

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