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This study aims to analyze the impact of technology-based corporation relocation on housing price indices during COVID-19 within the metropolitan areas of Austin, Texas and Seattle/Bellevue, Washington.The corporations under observation were Tesla and Amazon, respectively. The analysis intends to understand economic drivers behind the housing market and the radius of its effect while including fixed and random effects.

This study used a difference-in-difference (DID) method to evaluate changes in housing price index near and further away from Tesla’s and Amazon’s new corporate locations. The DID method allows for the capture of unique regional characteristics, as it requires a treatment and control group: housing price index and 5-mile and 10-mile search radii centered from the new corporate location.

The results indicated that corporate relocation announcements had a positive effect on housing price index post-pandemic. Specifically, the effect of Tesla’s relocation in Austin on the housing price index was not concentrated near the relocation site, but beyond the 5- and 10-mile radii. For Seattle/Bellevue, the effect of Amazon’s relocation announcement on housing price index was concentrated near the relocation site as well as beyond a 10-mile radius. Interestingly, these findings suggest housing markets incorporate speculation of prospective economic expansion linked with a corporate relocation.

Previous literature assessed COVID-19 housing market conditions and the economic effects of corporate relocation separately, whereas this study analyzed the housing price effects of corporate relocation during COVID-19. The DID method includes spatial and temporal analyses that allow for the impact of housing price to be observed across specified radii rather than a city-wide impact analysis.

Many studies have shown that the intensity and the number of hurricanes are likely to increase. This paper aims to look at the immediate effects of hurricanes on the time on the market, share of houses sold and percentage of houses with price cuts in the housing market using the metropolitan statistical area-level data in Florida.

Using a difference-in-difference method, the authors estimate the impact that a hurricane has on the housing markets.

The authors find that a hurricane has a positive and significant effect on the time on the market. A hurricane leads to a delay of the sale of a typical house in Florida by five days. The authors test for within-year seasonality and show that these effects change with seasonality of the housing market. Markets with seasonal housing prices tend to be affected more by hurricanes than those where housing prices are not seasonal. The authors also show that effects of a hurricane are transient and fade away in a few months. The results remain significant as the hurricane intensity changes.

This is the first study to look at the short-term effects of the hurricanes and how their effects vary based on seasonality of the markets.

The purpose of this study is to carry out a comparative analysis of four machine learning models such as linear regression, decision trees, k-nearest neighbors and support vector regression in predicting housing prices in Prishtina.

Using Python, the models were assessed on a data set of 1,512 property transactions with mean squared error, coefficient of determination, mean absolute error and root mean squared error as metrics. The study also conducts variable importance test.

Upon preprocessing and standardization of the data, the models were trained and tested, with the decision tree model producing the best performance. The variable importance test found the distance from central business district and distance to the road leading to central business district as the most relevant drivers of housing prices across all models, with the exception of support vector machine model, which showed minimal importance for all variables.

To the best of the author’s knowledge, the originality of this research rests in its methodological approach and emphasis on Prishtina's real estate market, which has never been studied in this context, and its findings may be generalizable to comparable transitional economies with booming real estate sector like Kosovo.

Housing market research involves observing the relationships between housing value and its indicators. However, recent literature indicates that the disruption of the COVID-19 pandemic could have an impact on the forecasting properties of some of the housing indicators. This paper aims to observe the relationships between the home value index and three potential indicators to verify their forecasting properties pre- and post-COVID-19 and provide general recommendations for time series research post-pandemic.

This study features three vector autoregression (VAR) models constructed using the home value index of the USA, together with three indicators that are of interest according to recent literature: the national unemployment rate, private residential construction spending (PRCS) and the housing consumer price index (HCPI).

Unemployment, one of the prevalent indicators for housing values, was compromised as a result of the COVID-19 pandemic, and a new indicator for housing value in the USA, PRCS, whose relationship with housing value is robust even during the COVID-19 pandemic and HCPI is a more significant indicator for housing value than the prevalently cited All-Item consumer price index (CPI).

The study adds residential construction spending into the pool of housing indicators, proves that the finding of region-specific study indicating the unbounding of housing prices from unemployment is applicable to the aggregate housing market in the USA, and improves upon such widely accepted belief that overall inflation is a key indicator for housing prices and proves that the CPI for housing is a vastly more significant indicator.

The purpose of this study is designed to achieve the learning process in producing studies involving economic issues and scenarios in business management in Malaysia. In addition, this study will provide exposure to the integration of managerial skills by using both microeconomics and macroeconomics concepts and theories to aid decision-making in a business environment.

The research method comprised qualitative methodology of literature review, case study and quantitative methodology of multiple linear regression (MLR). In this case, seven microeconomics and macroeconomics factors which are believed to significantly affect house price index (HPI) are taken into consideration which includes gross domestic product, consumer price index (CPI), government tax and subsidy on housing, overnight policy rate, unemployment rate (UNEMP), the median income (INC) and cost of production index.

This research has resulted in three significant factors affecting HPI from MLR, which include CPI, UNEMP and INC where the increase of these factors will cause a high increment of HPI. The other four factors are not significant.

Malaysia has been facing the stagnancy in house market these recent years due to issues such as massive oversupply, impacting Malaysia’s economy specifically focusing on domestic direct investment. To avoid oversupply issues, the vitality of future house demand and pricing forecast should be comprehended by involved bodies for more effective planning for the house development industry. To make a better and bigger impact, this research is intended to analyse the microeconomic and macroeconomic factors affecting the HPI to better understand the significance of each of these factors to the changes of HPI to resolve these economic issues.

While the declining rate of urban security and its potential effects have been globally acknowledged, the ways urban neighborhood security shapes real estate markets in African cities remain largely unexplained. The purpose of this paper therefore is to present the findings from a study of the nexus between urban neighborhood security and home rental prices in Lagos, Nigeria.

This paper is based on the hedonic price theory, an objectively derived urban neighborhood security index (UNSI) and property rental price data in Ojo, Lagos, Nigeria. This is a quantitative cross-sectional study that employs multistage sampling survey procedure. Data are analyzed using descriptive statistics, nonparametric correlation and hedonic price function with ordinary least squares (OLS).

Results show that nearly 50% of the study area is prone to insecurity and average rental values in Ojo, Lagos range from N151329.41 ($302.66) to N167333.33 ($334.67) per annum. Correlation analysis shows that home rental prices have high, positive and significant correlations (rs = 0.725 and p < 0) with UNSI. After controlling for neighborhood and structural factors, it is found that urban neighborhood security positively influences home rental values as a unit improvement in security leads to N81000.00 ($162.00) increase in rental value per annum.

Urban neighborhood security risk threatens residential property values, creates unintended residential mobility and destabilizes families. Findings from this study point to the facts that security is a key component of urban housing values and developers, and real estate investors must ensure that this component is well factored into property design, construction and valuation.

This is perhaps the first study that uses an objectively derived UNSI to study home rental price dynamics in Nigeria. The study extends knowledge on urban housing price determinants and contributes to literature on the crucial place of security in property management.

This study aims to establish the relationship between house acquisitions by foreigners (HAF) and house price index (HPI) in Turkey.

Due to the nature of this study, the data spans from January 2020 to March 2022. The house price index and the number of foreign house acquisitions across three provinces: Ankara, Izmir and Bursa, and national-level data were obtained from the TurkStat database. Consumer price index (CPI) and Turkish interest rates are control variables. In addition, monthly Turkish interest rates and CPI were obtained from the investing.com and TurkStat database, respectively. Furthermore, this study used autoregressive-distributed lag and Toda Yamamoto Granger causality models to avoid analysis bias. HPI and HAF are the variables used to accomplish the objectives of this study.

This study established a short-run equilibrium between foreign house acquisitions at the provincial and national levels. The short-run deviations were adjusted faster, ranging from 57.53% to 89.24% for some provinces, while Izmir is struggling to adjust at 6.48%. Both unidirectional and bidirectional Granger causality evidence suggests that the Turkish house price index increases at the national and provincial levels. This finding suggests the need for continuous policy intervention in the Turkish housing market because house prices play a pivotal role in Turkish economic development and daily lives.

This study’s scope and single-country study are its limitations. However, those limitations make the findings appropriate for the country of the study rather than generalising the results.

The study provides empirical evidence that foreign housing acquisition contributes negatively to housing affordability in Turkey and calls for authority intervention. This is because housing is considered shelter, a fundamental need to which citizens are expected to be entitled. Most citizens are low- and medium-income earners who may be unable to afford a house out of their income if it becomes costly. Once the expenditure to secure housing exceeds 30% of their income, it is considered unaffordable.

To the authors' best knowledge, this is the first empirical study that established the influence of foreign house acquisitions on Turkish house price increases and adversely reduced house affordability by Turkish citizens. The study is the first on foreign Turkish housing acquisition that used both theory of ownership and justice motivation theory to explain HAF.

The purpose of this study paper is to highlight certain related barriers and issues of housing affordability and examine the factors that influence housing affordability in Malaysia.

This study used panel data including several variables, namely, household expense, population, home financing, interest rate, inflation rate (IF) and rental rate (RR). The regression models of panel data, namely, the ordinary least square model, the fixed effects model and the random effects model, were evaluated for their suitability.

The findings revealed that RR and IF have a positive and significant impact towards housing affordability. The results provide strong evidence that RR as alternative in determining the home affordability as it helped in reducing the cost and the financing duration period of houses while at the same time increasing the level of capability of homeownership. Meanwhile, the level of IF has positive and significant impact towards housing affordability because it will cause a drop or increase in the purchasing power of households, as well as a decline or increase in the capability to own a house.

The most significant aspects to consider when analysing housing affordability in Malaysia are demand and supply. However, this study focuses on only five variables and only covers Malaysia. As a result, future researchers should analyse the study’s location, such as by region or district, and include additional variables from both the demand and supply sides. Homeownership of affordability requires a broader and more realistic definition in the current context of a more disruptive environment where technology such as fintech, blockchain and the internet of things acts as enablers for not only promoting homeownership but also ensuring homeownership sustainability. As a result, democratising Islamic home financing appears to be a viable option that requires rethinking, and further research is recommended.

The study proposes an end-to-end solution to promote homeownership levels by considering the level of RR as significant variables among stakeholders such as the house buyers/owners, sellers, investors as well the government agencies in influencing affordability in Malaysia.

This paper discusses the indicators of housing affordability index over the 21-year period of 2000–2020, covering all states in Malaysia. The comparison of affordability level can be seen through all states and by regions. Besides that, the findings revealed that RR and IF have a positive and significant impact towards housing affordability. RR is considered an essential variable in promoting homeownership in Malaysia and warrants further investigation towards policy implication. This paper also provides contribution on data on RR by states in Malaysia that can be used by policymakers to some extent.

Scholars note there are limited studies analyzing ticket price determinants. Using the common seat approach, the authors sought to advance this line of research by analyzing determinants of National Basketball Association (NBA) ticket prices in the secondary ticket market. The authors’ research seeks to ask two questions. The first is how ticket prices in the secondary market are associated with common determinants of consumer demand. The second question is what impact the COVID-19 pandemic has on ticket prices in the secondary market.

Ticket prices of NBA regular season games in the 2021–2022 season were collected a week before the game day from Ticketmaster.com. A regression model was estimated with a group of independent variables: income, population, consumer preference, quality of viewing, quality of contest and pandemic (the number of COVID-19 cases).

Results indicate income, population, consumer preferences (e.g. team quality and star players) and quality of viewing (e.g. arena age and weekend) impact prices. Further, the number of COVID-19 cases did reduce the ticket price.

The present study illuminates the theoretical significance of analyzing ticket prices as a proxy of demand in professional sport, while providing practical implications regarding the potential opportunity to increase revenue.