Real House Research Articles

An examination of the pass‐through of disaggregated energy prices to real house price: Evidence from the United States

Our study investigates the dynamic pass‐through of energy prices (crude oil price, electricity price, natural gas price, and coal price) to real house price in the United States using the data from 1970 to 2017. Based on the autoregressive distributed lag (ARDL) model, the empirical results suggest an incomplete pass‐through for all the energy prices to real house price both in the long run and short run except for long‐run pass‐through of crude oil price which is complete with statistically insignificant parameter. The Granger causality results reveal a feedback effect between natural gas price and real house price, output growth and real house price, natural gas price and crude oil price, coal price and electricity price, and output growth and coal price. In addition, a unidirectional causal relationship is found running from crude oil price, natural gas price, real house price, and coal price to electricity price. Again, we find that crude oil price is the cause of coal price in Granger sense. Therefore, our findings provide insights into proper design of energy policy that reduces the transmission of energy price shocks to house price in the United States.

The Impact of Uncertainty on State-Level Housing Markets of the United States: The Role of Social Cohesion

While considering the role of social cohesion, we analyse the impact of uncertainty on housing markets across the 50 states of the United States, plus the District of Columbia, using the local projection method for panel data. We find that both short-term and long-term measurements of macroeconomic and financial uncertainties reduce real housing returns, with the strongest effect originated from the macro-economic uncertainty over the long term. Moreover, the degree of social cohesion does not change the nature of the impact of uncertainty on real housing returns dramatically, but the size of the negative effects is relatively large for states with low social cohesion.

Housing price appreciation and economic integration in a transition economy: Evidence from Kazakhstan

This paper explores patterns of real estate price movements in an emerging upper-middle income economy, Kazakhstan. The country experienced an explosive, 11-fold increase in real housing prices in urban areas between 2000 and 2007, followed by a sharp decline and stabilization. This paper traces the movements across different regions, types of housing, unit size categories, and neighborhood types. We find that prices moved together closely, implying a linked, if not unified housing market, along with wealth effects that were felt broadly throughout the urban economy.

UK house prices and three decades of decline in the risk-free real interest rate

SUMMARY House prices have risen substantially faster than the prices of consumer goods in most G7 countries over the past few decades. This raises major policy issues: such rises affect the distribution of wealth within and between generations, the mobility of labour and financial stability. This paper explores why such rises have happened, and what the policy implications are. The price rises have been greatest in the United Kingdom, where real house prices have risen more than three and a half times since the 1970s, substantially outpacing real income growth. Meanwhile, rental yields have been trending downwards—particularly since the mid-90s. This paper reconciles these observations by analysing the contribution of a number of drivers of house prices. It shows that the rise in house prices relative to incomes between 1985 and 2018 in the United Kingdom can be more than accounted for by the substantial decline in real risk-free interest rates over the period. This is slightly offset by net increases in home-ownership costs from higher rates of tax. Changes in the risk-free real rate are likely to have been a major driver of changes in house prices. We analyse why they have driven house prices up faster in the United Kingdom than in other advanced economies. Our model predicts that a 1% sustained increase in index-linked gilt yields from current rates could ultimately result in a fall in real house prices of around 20%.

Investigating the causes of increasing consumer demand for real estate housing in Bangladesh

Purpose The purpose of this paper is to find out the causes of increasing population in the real estate area. The demographic in information of the respondents and the level of satisfaction was also carried out for this study. Design/methodology/approach The authors use both primary and secondary data. Total 329 respondents were surveyed at the real estate area after completing sample size determination. Secondary data was collected from journals, real estate offices and papers. After that, using regression and correlation analysis, the data was analyzed and finalized. Findings This study identified migration as the most critical variable. The study determined ten hypotheses and only accepted two. By that, this study finds out the causes of the increasing demand of plots and flats in real estate. Originality/value This study will work as a baseline study for the real estate sector in Bangladesh. Most of the research on Bangladesh’s real estate is done mainly on real estate market assessment and consumer satisfaction. Nevertheless, this study will find out the causes of the increasing population in real estate.

A blind event-based learning algorithm for non-intrusive load disaggregation

Non-intrusive loading monitoring (NILM) provides a smart solution to the problem of electrical energy monitoring of households at the appliance level. In blind disaggregation, the power level of each appliance is not known a priori. In this paper, we propose an event-based blind disaggregation algorithm that uses Gaussian mixture models (GMM) for clustering to automatically detect two-state appliances from the aggregate data. The benefit of using Gaussian mixture models over other clustering methods is that they can automatically learn the statistical distributions present in the data. This is beneficial, especially when the appliances have similar power consumptions. Since Gaussian mixture models do not determine the number of clusters automatically, we use Bayesian information criteria (BIC) to determine the number of clusters. The blind disaggregation method is tested with data from a real house collected by a smart meter which samples the aggregate consumption at 3.4 kHz and also from Reference Energy Disaggregation Dataset (REDD) public data, sampled at a frequency of 1 Hz. We compared the performance of our algorithm with other unsupervised methods and found comparable performance. We also compared the Gaussian mixture model with mean shift clustering in a blind disaggregation. We saw an improved performance by using Gaussian mixture models instead of mean-shift clustering in various accuracy measures.

Evaluation of the Thermic Efficiency of the Prototype at Scale of a Sustainable Housing that Uses Concrete with PET Fibers (CFP) and the Trombe System

In the last century, climate change has been one of the most notorious consequences of global warming. These changes are manifested through extremely warm or icy temperatures. Being the latter, the one that affects Andean populations in diverse parts of the world. In view of this, many researchers propose various techniques or methods to improve the conditions of habitability and thermic comfort to confront these cold temperatures. This study conducts an experimental investigation of the improvement of thermic efficiency that reaches a prototype of housing to include polyethylene terephthalate (PET), as a component of the concrete mixture in the structural elements. For this purpose, studies were reviewed that allowed selecting the most optimal considerations, with respect to the elaboration and inclusion of PET in the mixture. In addition, the proposed technique includes an adaptation and innovation of the trombe system in the roof, in order to optimize the thermic properties. The results in the present study show that this combination has allowed us to considerably improve the thermal efficiency properties in the prototype of the housing, which is an indicator of its performance when it is applied to real scale housing.

HOUSING PRICE BUBBLES AND FACTORS AFFECTING THE FORMATION OF BUBBLES: THE TURKISH CASE

Price bubbles may be a leading indicator for financial crises. In history, some of the important price bubble cases occurred in the real estate or housing market. In this study, the existence of a price bubbles, the periods of the existing bubbles, and the factors affecting the formation of bubbles in the housing sector for both Turkey and TR22 Region (Balıkesir-Çanakkale) are being investigated. Data covering 126 monthly housing price indices for the period between January 2010 and June 2020 were used. According to the SADF and GSADF test results, some evidence of housing bubbles for both Turkey and TR22 Region was reached. According to the results of the logit model applied in the study, the increase in the housing loan volume and the real estate interest rates as well as the decrease in the BIST-100 and M2 money supply, increase the probability of a housing price bubble for the TR22 Region. Additionally, the increase in the housing loan volume, real estate interest rates, BIST-100 and CPI as well as the decrease in the industrial production index, increase the probability of housing bubbles for Turkey. Considering those factors, the regulatory authorities in Turkey such as the BRSA and CBRT should take preventive measures to maintain price, financial and economic stability. In addition, those who invest in the housing sector should carefully monitor the existence and causes of price bubbles in this sector.

Leaning against house prices: A structural VAR investigation

Evidence from monetary VARs suggests that in the U.S., Canada, and the U.K. the impact of monetary shocks on real house prices is about three to five times as large as that on real GDP. Although these trade-offs are not manifestly unfavorable, in the light of the large differences in the magnitudes of house prices and GDP fluctuations, a monetary policy of leaning against the former would inevitably entail significant losses in the latter. I use the identified VARs in order to explore the corresponding trade-offs associated with a monetary policy of weakly, but systematically leaning against house prices. Results from ‘modest’ (in the sense of Leeper and Zha, 2003) policy counterfactuals suggest that, in population, the impact on real house prices is about three times as large as that on real GDP for all of the three countries. Within the specific context of the upsurge in U.S. house prices which pre-dated the financial crisis, a shortfall of one per cent of GDP would have been associated with a decline in real house prices by about four per cent.

Credit expansion, bank liberalization, and structural change in bank asset accounts

This paper studies the links among credit supply expansion, commercial bank asset account structures, and the housing boom preceding the 2007–2009 financial crisis. We propose a real business cycle model with a housing market and financial intermediaries (banks) subject to leverage constraints. In our model, banks channel funds to firms for production and provide collateralized loans to mortgage borrowers; thus, banks determine their asset account structures endogenously. We show that a credit supply expansion to banks can account for four key facts that characterize the housing boom: (1) an increase in real house prices; (2) an increase in the mortgage-to-GDP ratio; (3) a decrease in the real mortgage interest rate; and (4) an increase in the ratio of mortgages to firm loans in commercial bank asset accounts. In our model, a credit supply expansion to banks can also generate a boom-bust cycle through the collateral value channel via mortgage borrowers. Asset-side bank regulations that reduce excessive mortgage issuance during a credit boom can help to dampen the subsequent economic downturn.

Time-varying spillovers between housing sentiment and housing market in the United States☆

This paper investigates spillovers between the housing sentiment index of Bork et al.(2020), common factors in US real housing returns and their volatility, GDP growth and real interest rates. We find that in contrast to spillovers from the common factor of housing returns to housing sentiment and GDP, reverse spillovers are relatively weak. This suggests that, while a shock to housing prices is likely to have a significant impact on housing sentiment and the economy, a purely exogenous shock to housing sentiment may in itself have little impact on housing returns and volatility.

What is in a name? A modern interpretation from housing price in Hong Kong

ABSTRACT Hong Kong is a metropolitan city. Owing to its historical background as a former British colony, it is characterised by a mixture of Chinese and Western cultures. While most housing estates’ names are bilingual in Chinese and English, many recent large scale housing estates have an English name only. Do housing estates with an English name only associate with higher prices than bilingual ones? To the best of our knowledge, none have studied that before. We attempted to fill this research gap. Four large-scale residential projects in Yuen Long District were selected, two are bilingual and two have English names only. 253,605 data points, including macroeconomic variables and housing characteristics, were included in this study. The results of the hedonic price model showed that properties with English names only were associated with a higher price than bilingual ones. We also utilised a long short-term memory (LSTM) housing price prediction model to predict housing price. Our research results show that housing estates with English names only are associated with higher housing price. It provides a new perspective on real estate housing estates’ brand management and provide a reference for real estate buyers in the future.

Real-Time Non-Intrusive Electrical Load Classification Over IoT Using Machine Learning

In this era of technological advancement, the flow of an enormous amount of information has become such an inevitable phenomenon that makes a path for the takeover of the internet of things (IoT) based smart grid from the currently available grid system. In a smart grid, demand-side management plays a crucial role in reducing the generation capacity by shifting the user energy consumption from peak period to off-peak period, which requires detailed knowledge of the user consumption at the individual appliance level. Non-intrusive load monitoring (NILM) provides an exceptionally low-cost solution for determining individual appliance levels using a single-point measurement. This paper proposed an IoT-based real-time non-intrusive load classification (RT-NILC) system considering the variability of supply voltage using low-frequency data. Due to the unavailability of smart meters at the household level in Bangladesh, a data-acquisition system (DAS) is developed. The DAS is capable of measuring and storing rms voltage, rms current, active power, and power factor data at a sampling rate of 1 Hz. These data are processed to train different multilabel classification models. The best-performed classification model has been selected and utilized for the implementation of RT-NILC over IoT. The Firebase real-time online database is considered for data storage to flow the data in two-way between end-user and service provider (energy distributor). The GPRS module is used for wireless data transmission as a Wi-Fi network may not be available everywhere. Windows and web applications are developed for data visualization. The proposed system has been validated in real-time, using rms voltage, rms current, and active power measurements at a real house. Even under supply voltage variability, the performance evaluation of the RT-NILC system has shown an average classification accuracy of more than 94%. Good classification accuracy and the overall operation of the IoT-based information exchange systems ensure the proposed system’s applicability for efficient energy management.

Housing Refurbishment for Energy Efficiency and Comfort

The housing stock in Vietnam has boomed in the last few decades, especially in urbanised areas. However, the increasing number of housing units did not go along with housing quality, a healthy living environment or a sustainable building stock. Recent legislation only applies to public buildings but not the private housing sector, which accounts for the majority of the building stock. Therefore, this research aimed to contribute to a more sustainable building stock in Vietnam by improving the energy efficiency in new and renovated urban houses. This research started with examining the energy upgrade potential of the existing houses in Vietnam. Both passive and active refurbishment design measures were investigated for the Vietnamese context. Among the measures, a green facade has a large potential in energy saving. Effect of a green facade on thermal and energy performance was tested by conducting a physical experiment on a real tube house in Hanoi. Next, a stepped design strategy was introduced in a student design workshop in Vietnam. The participants were trained to apply sustainable and energy efficient design measures for Vietnamese tube houses. In addition, the vision for designing future tube houses was discussed on several sustainability aspects: urban densification, energy efficiency, circular economy and social interaction. This research is also expected to contribute to the establishing of a future national technical regulation for private housing in Vietnam.

Real estate price model based on multiple linear regression and analysis of influencing factors of regional economic vitality development

Using this statistical analysis method, which regards one variable as a dependent variable and one or more other variables as independent variables, the quantitative relations of linear or nonlinear mathematical models between multiple variables are established and analyzed by using sample data. Economic vitality is the regional support to its social economy. By analyzing the influencing factors of economic vitality, the evaluation index system of regional economic vitality is constructed. SPSS software is used to analyze the selected data, so as to judge the change of gross national product, which is of great significance for improving the efficiency of government policy implementation, stimulating urban economic vitality and realizing regional sustainable development. Taking Guangzhou as an example, through multiple regression analysis, this paper establishes a model of influencing factors from the perspectives of human capital level, government intervention, consumption, industrial structure and opening up, and puts forward an action plan to improve regional economic vitality. This paper studies the relationship between the average sales price of commercial housing and the sales area of commercial housing, real estate housing investment, year-end resident population, regional gross domestic product and the number of real estate development enterprises. a multiple linear regression model for predicting commercial housing prices is established, and the model is tested and tested for multicollinearity, serial correlation and heteroscedasticity, and the optimal model is obtained. In addition, based on the grey correlation degree, this paper analyzes the impact of population trend and enterprise vitality on the economic vitality of Guangzhou, and studies the long-term and short-term impact of economic policy transformation on the provincial economy.

Global liquidity, global risk appetite, and the risk of credit and asset booms

This study examines the channels through which net cross-border bank flows and VIX, working through the domestic banking system, could potentially lead to the creation or exacerbation of credit and asset booms that may threaten financial stability. It uses bank firm-level data for the Philippines over the period 1991-2018. Among the study’s significant findings are the following: bank lending to the real estate and housing sector is consistently and significantly affected by net cross-border bank flows (NCBF); non-core bank liabilities is an important variable as it consistently affects the amount of total loans and loans to the commercial and industrial sector as well as bank asset growth and bank leverage; net cross-border flows lower bank asset growth; and banks’ cost of loans tends to be reduced by NCBF and by a reduction in global risk appetite. The use of macroprudential measures-including monitoring bank non-core liabilities and lending to sectors such as real estate-as well as capital flow management measures is warranted in order to prevent bank credit and asset booms from being created or exacerbated, which may threaten growth and financial stability.

Financial Geographies of Real Estate and Housing

AI Publications is an open access peer review international journal to publish the article in the field of Agriculture, Horticulture, Food science, Rural Development, Environment and Health Research, Chemistry, Mathematics,Physics.

Mortgage and mortgage lending as a tool for financial recovery of the modern Russian economy

The article is devoted to the institutional analysis of the problem of improving the legal regulation of mortgage relations in Russia in modern conditions in the presence of a rather limited financial capacity of the population in modern conditions to purchase real estate or housing. In this regard, the mortgage becomes the most necessary tool to ensure the socio-economic development of society, its stable and sustainable development. The formation of the property base currently generates a corresponding demand in the real estate market, and develops a corresponding interest among consumers in mortgage lending. At the same time, the emerging financial mechanism of mortgages also ensures the formation of the solvency of not only the population, but also other market participants, so that this market segment becomes not only in demand, but also successfully developed. Considering this problem, the author sought to update the scientific, theoretical and practical interest in this topic and to reveal the problems that hinder the development of mortgages as an important factor in the financial recovery of the modern market economy.

Study on the Spatial Differentiation Pattern of Housing Prices in Chengdu-Chongqing City Cluster

Taking the housing prices of 151 districts and counties in the Chengdu-Chongqing urban agglomeration from 2017 to 2019 as the basic data, the housing price data is geospatially expressed in ArcGIS, and the Theil index is calculated to analyze the differentiation pattern of the housing prices of the Chengdu-Chongqing urban agglomeration, using geo-detector. The effect of different factors on housing prices in different regions shows that the geographical distribution of housing prices in the Chengdu-Chongqing urban agglomeration is basically the same as the city scale and economic development of each city. The housing prices of the Chengdu-Chongqing urban agglomeration show a dual-core dominance and a collapse in the central region. The characteristics of industrial structure, housing supply, population scale, land cost, economic strength, real estate investment scale, housing policy, and higher education resources have a significant impact on the housing prices of Chengdu-Chongqing urban agglomeration. Different indicators have different effects on housing prices in different cities.

Housing Refurbishment for Energy Efficiency and Comfort

The housing stock in Vietnam has boomed in the last few decades, especially in urbanised areas. However, the increasing number of housing units did not go along with housing quality, a healthy living environment or a sustainable building stock. Recent legislation only applies to public buildings but not the private housing sector, which accounts for the majority of the building stock. Therefore, this research aimed to contribute to a more sustainable building stock in Vietnam by improving the energy efficiency in new and renovated urban houses. This research started with examining the energy upgrade potential of the existing houses in Vietnam. Both passive and active refurbishment design measures were investigated for the Vietnamese context. Among the measures, a green facade has a large potential in energy saving. Effect of a green facade on thermal and energy performance was tested by conducting a physical experiment on a real tube house in Hanoi. Next, a stepped design strategy was introduced in a student design workshop in Vietnam. The participants were trained to apply sustainable and energy efficient design measures for Vietnamese tube houses. In addition, the vision for designing future tube houses was discussed on several sustainability aspects: urban densification, energy efficiency, circular economy and social interaction. This research is also expected to contribute to the establishing of a future national technical regulation for private housing in Vietnam.