House Sales Research Articles

Residential Marketing Duration: Film Studios as Neighborhood Sales Accelerators

One measure of residential market liquidity is the speed at which houses sell. Most research about listing duration focuses on agent marketing activities, while external neighborhood changes that could influence a house sale are usually only examined in terms of their price impacts. This paper considers how opening a cultural industry facility influences the likelihood of sale and marketing duration for houses in surrounding neighborhoods. We employ a probit model to quantify the effect of opening large film studios in the Atlanta metropolitan area on the probability of sale and a hazard model to explore the influence on marketing duration in nearby neighborhoods. A difference-in-differences approach allows us to compare market results before and after the studio openings. We find that the likelihood of sale increases while marketing duration decreases for houses located within 1.5 mi of a large film studio once it opens. Thus, the film studios exert positive externalities on the surrounding residential neighborhoods. The positive response may be attributable to film industry workers attracted by convenience and industry buzz as well as other house buyers who are excited about being associated with the film industry or believe the presence of the studio generally improves the neighborhood.

Property Management System

Abstract: The price of a house is increased every year according to the location. It indicates the current economic situation so there is a need for a system to predict house sales in the future for both buyer and the seller. Here we use a dataset of Pune with more than 68,613 entries of train data and test data of housing sales in India. This analysis includes the effect of markdowns on sales and the extent of effects on the sales by size, price, area etc. has been analysed using different machine learning algorithms. Estimating home sales can help the developer determine the selling price of the home and the best time for the buyer to purchase the home. The output values of the algorithms are estimated based on the input characteristics from the data presented in the system and the analysis is a process. Physical conditions, concept and location are the three factors that determine the selling price of a property.

Utilizing Multilevel Modeling to Measure Neighborhood Dynamics and Their Impact on House Prices

For decades, researchers have been concerned with house price modeling, and recognition has grown for the necessity of considering environmental and contextual variables in the process. This research examines the association between neighborhood characteristics and all individual house sale prices in Lucas County, Ohio, from 2012 to 2016, through a multilevel modeling (MLM) approach. Although there are various ways to define neighborhoods, census tracts and school districts are used in this study. Neighborhood characteristics include a foreclosure score, race heterogeneity, median household income, and built environment variables, such as walkability indexes. School district characteristics include student performance, tuition expenditures per pupil, and percent of expenditures spent on classroom instruction. The advantage of the multilevel model, is that it allows us to derive reliable estimates of place differences, representing a considerable improvement over the single-level model. Significant correlations were identified between house prices and foreclosure score, student performance, and tuition expenditures per pupil. The MLM results indicate that house prices not only lay in house characteristics themselves but also in neighborhood features, thus MLM offers good prediction accuracy and high explanatory power.

Policy pressure to retrofit Germany's residential buildings to higher energy efficiency standards: A cost-effective way to reduce CO2 emissions?

Germany's climate goals require over 75% of its housing stock to be retrofitted for energy efficiency. An important question for Germany and other countries is, what minimum energy efficiency standard should be mandated for housing retrofits so as to reduce CO2 emissions economically efficiently? It has long been known that the marginal costs of energy efficiency increase as standards are pushed higher, and therefore the marginal costs of CO2 abatement increase, but at the same time energy consumption reduces, thereby reducing CO2 emissions. This paper investigates where the social optimum lies between these two effects. In addition to a large database of house sale advertisements containing price and energy efficiency data, it uses two semi-official data sources of modelled and actual energy efficiency upgrades in Germany. It finds that retrofitting to a modestly high standard of about 70 kWh/m2/y entails marginal costs of CO2 abatement comparable to current CO2 prices. However, each step to a higher standard increases these by up to an order of magnitude, with absurdly high marginal costs of CO2 abatement for the highest standards. German policymakers are currently pushing for higher minimum standards, but money could better be spent retrofitting more houses to less stringent standards.

Two-stage communication-efficient distributed sparse M-estimation with missing data

Distributed estimation based on different sources of observations has drawn attention in the modern statistical learning. When the distributed data are missing at random, we propose a two-stage -penalized communication-efficient surrogate likelihood (CSL) algorithm based on inverse probability weighting to eliminate the estimation bias caused by the missing data and construct sparse distributed M-estimator simultaneously. In the first stage, we consider a parametric propensity model and directly apply the -penalized CSL method to obtain an efficient and sparse distributed estimator of the propensity parameter. In the second stage, we construct an IPW-based -penalized CSL loss function to eliminate the bias and obtain the sparse M-estimation. The finite-sample performance of the estimators is studied through simulation, and an application to house sale prices data set is also presented.

Decomposing the impact of immigration on house prices

How does an increase in immigrant inflows affect housing demand and prices for a given housing supply? In this paper, I show that we can formally decompose total demand changes into those from the immediate increase in population due to the new arrivals (the “partial effect”) and additional changes from relocated natives (the “induced effect”). I propose and apply a method to estimate these effects separately, exploiting data for Spain between 2001 and 2012. Using an instrumental variables strategy, I find that a one percentage point increase in the immigration rate raises average house sale prices by 3.3%. Partial demand estimates are 24% lower than total estimates due to immigrants and natives locating in the same provinces. The results show that accounting for the impact of immigration on native mobility is central to understanding net demand adjustments, as partial and total effects can significantly differ depending on native population relocation.

Perspectives of Southwest Florida Homeowners and Real Estate Agents before Hurricane Ian

In September 2022, Hurricane Ian made landfall in southwest Florida, causing an estimated $67 billion in damage and the loss of almost 150 lives. Before this date, demand and house prices in this area were rising faster than anywhere else in the country. What did homeowners in southwest Florida believe about flood risk to their own homes, and what did real estate agents believe about the role of flood risk in the residential housing market? The survey research summarized in this article shows that not only did residents feel that they were not particularly at risk from flooding, but also that damaging floods would not affect future home values in their neighborhoods or for their own homes. We found that political party affiliation was strongly correlated with the direction and strength of these beliefs. Real estate agents also reported strong demand for low-elevation coastal housing, noting that coastal lifestyle outweighed the prospect of flooding in residential decision-making. They also observed that the detailed maps of flood risk that are now available at realtor.com for anyone browsing for houses for sale had no effect on homebuyers, and that most of their customers were generally unconcerned with flood risk.

STRATEGI PEMASARAN PT. MITRAGAMA INTIPERKASA UNTUK MENINGKATKAN PENJUALAN UNIT RUMAH LA PALMA GRANDE BEKASI

This research studies marketing strategy for the increase of house sales at La Palma Grande, PT. Mitragama Intiperkasa according to product, location, price and promotion with several observations and interviews with related parties. Type of research was qualitative descriptive by selecting some information sourced and direct observation to the location. The result of this research state the product, location, price and promotion La Palma Grande greatly affect the increase in sales. The result of observation of the location of La Palma Grande are very strategic, residential locations directly access the main road, near the highway, education facilities, health facilities and hospital, shopping center, industrial area, business and office areas as well as tourist attraction. In terms of price, the results of interview respondents in La Palma Grande are very affordable for factory employees, civil servants, state-owned enterprises, entrepreneurs and private employees, starting price from 400 million in 2022.

미시적인 초등학교 통학환경이 주택가격에 미치는 영향

Recently, the issue of commuting safety for elementary school students is emerging, and research on this issue is necessary. However, research on the elementary school commuting environment has been mainly focused on accessibility through the straight-line distance between home and school, studies on the effect of microscopic commuting environment on housing prices are insufficient. Therefore, the purpose of this study is to analyze the effect of microscopic elementary school commuting environment on housing price. For this purpose, multiple regression analysis was used with the commuting environment as the independent variables and the housing price per unit area as the dependent variable. The subjects of the study were 324 apartment buildings in 27 apartment complexes in Mok-dong and Cheonggye-dong, Hwaseong-si, which have the highest ratio of elementary school students to the population. For the analysis data, 882 houses for sale registered in Naver real estate platform from July 16, 2021 to July 21, 2021 were used. The analysis results are summarized as follows. First, as the walking distance to the school increased, the housing price decreased. Second, if crossings were necessary for commuting to elementary school, housing prices decreased. Third, the wider the road to cross when commuting to school, the lower the housing price. Fourth, if a bicycle road with high bicycle speed exists in the commuter road, the housing price decreased. Summarizing these results, the microscopic elementary school commuting environment affects housing prices. In particular, variables related to traffic safety have a great influence on housing prices, and this should be considered in the site planning and apartment building arrangement process.

The Impact of Water Body Restoration on the Sales and Rental Markets in Beijing

<h3>Abstract</h3> Black and odorous water bodies (BOWBs) are increasingly common in cities worldwide. This paper evaluates the impact of an urban water bodies restoration program in Beijing, China, based on detailed housing sale and rental transaction data. We implement a hedonic price model with a difference-in-difference and a triple difference approach and find that listing and restoration of a BOWB significantly increase house prices by 2.2-3.9 percent and 2.3-4.9 percent, respectively, while at the same time increase rents by 11.3-13.7 percent and 13.4-16.9 percent, respectively.

The mundanity of cost cutting: The value of small wins in affordable housing production

Regardless of the best intentions to address the issue among policy makers, affordable housing remains one of the most underprovided assets in advanced economies, otherwise characterized by an ample supply of product offerings. The paper addressed the question of how affordable housing can be provided on basis of new housing production. The study is premised on the theoretical proposition that excellence is not primarily a matter of working “harder” or “smarter” than competitors do, but is rather an effect of small, yet discernable qualitative changes (being the basis for the management of the “mundanity of excellence”) that generate benefits and returns in excess of what may be originally expected. The value of such “small wins” are of general relevance for any organized activity or managerial pursuit. Drawing on a study of two low-cost producers operating in the Swedish market and (in one of the cases) abroad, it is shown that to make newly produced homes affordable (defined in local terms on basis of documented housing sales and their buying price), the planning, production, and sales cost need to be minimized throughout the whole process. Affordable housing is thus provided on basis of a full organizational and wider institutional commitment to serve households with limited budget headroom, and this work demands a long-term commitment to stated business objectives and enacted housing policies.

Investigation of Factors Affecting Housing Sales in Turkey in Terms of Covid-19 Period: Count Time Series Model

Türkiye’de ekonomik büyümenin önemli bileşenlerinden biri konut sektörüdür. Konut arz ve talebinin dengesinin sağlanmasında ise konut satış tahminlerinin yeni nesil tekniklerle doğru bir şekilde yapılmasına bağlıdır. Bununla birlikte, konut satış miktarına ait tahminler yapılırken incelenen dönemdeki ekonomik ve finansal krizler, salgın hastalıklar, doğal afetler gibi olağanüstü olaylar dikkate alınmalıdır. Bu çalışmanın amacı, Türkiye’de konut talebi ile makroekonomik değişkenler arasındaki ilişkinin kovid salgınının etkisini de dikkate alarak sayma zaman serisi analizi ile tahmin edilmesidir. Elde edilen bulgular, tüketici fiyat indeksi ve konut kredi faizlerinin konut satış miktarları üzerinde negatif bir etkiye sahip olduğunu; dolar kuru ve konut fiyat indeksinin ise konut satış miktarlarını pozitif etkilediğini ortaya çıkarmıştır. Kovid sonrası dönemde ise tüketici fiyat indeksindeki artışların konut satış miktarı üzerinde daha yüksek bir azaltıcı etkisi söz konusu iken; dolar kuru, konut fiyat indeksi ve konut kredi faizlerindeki artışların konut satış miktarı üzerinde pozitif bir etki yarattığı görülmüştür.

Consumer Protection in Housing Buying and Buying Loans At "Griya Kademangan Beautiful" Housing Demangan Siman Ponorogo Islamic Law Perspective

Housing is one of the basic human needs that must be met. Having your own home is everyone's dream, even a need for those who are already married. However, the soaring house prices have made it rare for people to be able to buy a house in cash, so they have to buy in installments or rent. In this study, the author uses a descriptive type of research. The data used in this research is secondary data by studying and analyzing legal materials and tertiary legal materials, namely legal materials that provide instructions and explanations for primary and secondary legal materials, namely: legal dictionaries and Indonesian language dictionaries. In this case, the researcher examines and analyzes related to legal protection in buying and selling housing on credit at Griya Kademangan Indah Demangan Siman Ponorogo housing from the perspective of Islamic law. The results of research in this writing are: In this housing use the concept of buying and selling sharia and the laws and contracts used are also different, some use Islamic law contracts and some also use contracts in law. The housing provided is also in the form of ready stock or ready-made materials, it is likely that the buyer feels satisfied in choosing the house to be inhabited, because the principle is that the buyer likes to offer what is expected by the buyer, because the conditions for a valid sale and purchase are mutual pleasure. In the sale and purchase of housing in housing "Griya Kademangan Indah" explained that in buying and selling this housing carries the concept of no usury, his can be seen in their implementation practices that they do not involve third parties, namely conventional banks and Islamic banks in their transactions, on the grounds that banks still have elements of usury. So, in payments made by consumers to housing owners, it can be directly to the housing owner, it can be content or cash and it can also be paid in installments directly to the housing owner in accordance with the agreed agreement because it does not contain an element of usury in it.

How prebound effects compromise the market premium for energy efficiency in German house sales

ABSTRACT Recent studies indicate that houses with higher energy efficiency usually have higher market prices, a ‘market premium for energy efficiency’. But in Germany the usefulness of this premium is confounded by the ‘prebound effect’: the gap between officially certificated energy ratings and actual energy consumption. Attempts have been made to close this gap from two complementary directions: downwards, by obtaining more accurate and less pessimistic technical estimates of idealised energy performance; and upwards, by estimating how much energy occupants realistically need for health and comfort. This study investigates prebound effects alongside an analysis of house prices in Germany, using a large database of house sale advertisements from 2007 to 2021, focusing on pre-1980 homes that were re-sold in 2019–2021. It uses ordinary least-squared multivariate regression to estimate market premiums for energy efficiency and sets these alongside estimates of prebound (and rebound) effects. It finds that prebound effects can lead purchasers to overestimate future energy savings and therefore pay more for properties than their actual worth. It also offers simple models to help purchasers interpret energy ratings more critically and estimate likely energy savings more realistically. Finally, it suggests how policymakers could modify energy ratings to reflect likely energy consumption more accurately.

House price prediction modeling using machine learning techniques: a comparative study

In the literature, there are two basic approaches regarding the determination of house prices. One of them is the prediction of house price using macroeconomic variables in the country where the house is produced, and another one is the price prediction models, which we can express as micro-variables, by considering the features of the house. In this study, the price of the house was attempted to be predicted using machine learning methods by establishing a model with micro variables that reveal the features of the house. The study was conducted in Turkey’ Antalya province, where household housing demand of foreigners is also high. The house advertisements in locations belonging to the lower, middle- and upper-income groups were selected as the sample. In the results, it was observed that the artificial neural network (ANN) method made predictions with more meaningful results compared to support vector regression (SVR) and multiple linear regression (MLR). These results appear to be a viable model for institutions that supply housing, mediate housing sales, and provide housing financing and valuation. It is considered that this model, which can be used to predict fluctuating house prices, especially in developing countries, will regulate the housing market.

The Importance of Digitization in Estimating Housing Fair Value with the Artificial Neural Networks Method: The Case of Yenimahalle/Ankara/Turkey

With the rapid development in the construction sector in recent years, housing sales, which is one of the economic investments, have accelerated, making an objective valuation difficult and making it impossible to predict the real price due to the fact that there are too many parameters in the valuation stage and there is no definite formula. In addition, in real estate appraisal processes, the use of artificial neural networks (ANN), which is one of the artificial intelligence methods, has made it attractive to perform and adapt machine learning using examples, to provide information about unprecedented examples, to work fast and easy to identify, to provides solutions to complex problems, to work with little information. In this study, ANN models were created by rearranging the quantification values in an existing study by creating ANN models with 14 parameters that are effective in determining the fair value of a total of 220 houses for sale advertised on an e-commerce site in different neighborhoods of Yenimahalle district of Ankara/Turkey, and it was observed that with the use of rearranged quantification values, the ANN architecture selected with mean square error (MSE) 0.000016, regression (R) 95.99% and accuracy rate 91.73% gave more successful results in predicting the house price.

Are we looking in the right place? Housing search mismatches: evidence from Greater Manchester in the UK

PurposeWith access to the large-scale search data from Rightmove plc, the paper firstly indicated the possibility of using user-generated data from online property portals to predict housing market activities and secondly embraced a GIS approach to explore what people search for housing and what they chose and investigated the issue of mismatch between search patterns and revealed patterns. Based on the analysis, the paper contributes a visual GIS-based approach which may help planners and designers to make more informed decisions related to new housing supply, particularly where to build, what to build and how many to build.Design/methodology/approachThe paper used the 2013 housing search data from Rightmove and the 2013 price data from Land Registry with transactions made after the search period and embraced a GIS approach to explore the potential housing demand patterns and the mismatch between searches and sales. In the analysis, the paper employed the K-means approach to group prices into five levels and used GIS software to draw maps based on these price levels. The paper also employed a simple analysis of linear regression based on the coefficient of determination to investigate the relationship between online property views and values of house sales.FindingsThe result indicated the strong relationship between online property views and the values of house sales, implying the possibility of using search data from online property portals to predict housing market activities. It then explore the spatial housing demand patterns based on searches and showed a mismatch between the spatial patterns of housing search and actual moves across submarkets. The findings may not be very surprising but the main objective of the paper is to open up a potentially useful methodological approach which could be extended in future research.Research limitations/implicationsIt is important to identify search patterns from people who search with the intention to buy houses and from people who search with no intention to purchase properties. Rightmove data do not adequately represent housing search activity, and therefore more attention should be paid to this issue. The analysis of housing search helps us have a better understanding of households' preferences to better estimate housing demand and develop search-based prediction models. It also helps us identify spatial and structural submarkets and examine the mismatches between current housing stock and housing demand in submarkets.Social implicationsThe GIS approach in this paper may help planners and designers better allocate land resources for new housing supply based on households' spatial and structural preferences by identifying high and low demand areas with high searches relative to low housing stocks. Furthermore, the analysis of housing search patterns helps identify areas with latent demand, and when combined with the analysis of transaction patterns, it is possible to realise the areas with a lack of housing supply relative to excess demand or a lack of latent demand relative to the housing stock.Originality/valueThe paper proves the usefulness of a GIS approach to investigate households' preferences and aspirations through search data from online property portals. The contribution of the paper is the visual GIS-based approach, and based on this approach the paper fills the international knowledge gap in exploring effective approaches to analysing user-generated search data and market outcome data in combination.

БУДІВЕЛЬНИЙ БІЗНЕС УКРАЇНИ: СУЧАСНИЙ СТАН, ПРОБЛЕМИ ТА ШЛЯХИ ЇХ ВИРІШЕННЯ

The construction industry plays an extremely important role in restoring the country's infrastructure destroyed as a result of hostilities, providing housing needs of the population, and has a significant impact on the development of the economy as a whole. The development of the country's economy and the well-being of its population depend on how efficient the construction business is. The operating conditions of construction enterprises in wartime are significantly complicated and differ from those in the pre-war period, requiring a study of the safety of the environment, determination of the condition and suitability for use of restoration objects. Therefore, the study of the current state, trends and prospects for the development of the construction industry, the identification of problems that hinder it, and the determination of ways to solve them are particularly relevant. The article analyzes the main indicators of the activity of the construction industry after the full-scale invasion of the Russian Federation into Ukraine. The dynamics and structure of the volumes of completed construction works, indexes of construction products, and the total area ofresidential buildings at the beginning of construction in comparison with the pre-war period have been determined. The trends of changes in the volume of construction of residential and non-residential buildings, engineering structures in 2023, which are due to the need to restore transport and industrial facilities, bridges, roads, overpasses, pipelines, railways, power lines, communications, and other structures destroyed due to Russian military aggression, have been clarified. The state of resumption of construction activity in the regions of Ukraine as of April 1, 2023 is characterized. The pace of commissioning and sale of housing in Ukraine as of April 1, 2023 has been determined. Indicators of creation of business entities – legal entities and individuals in the field of construction of residential and non-residential buildings by regions of Ukraine in the period from February 24, 2022 to December 31, 2022 are given. Prospects for the development of the construction industry were considered, challenges preventing it in the conditions of martial law were identified, and opportunities to overcome them were proposed.

Conceptual framework and system of the Model law of the Commonwealth of Independent States “On Protection of Consumers”

The article substantiates the need to improve the model consumer legislation of the member states of the Commonwealth of Independent States (CIS), which has become brewing in connection with fundamental transformational changes in social life, development of electronic commerce, the development of new standards upon sale of goods, works, services to consumers. The paper draws attention to the differences in processes of approximation (harmonisation) of the national legislations of the EU member states and approximation of laws of the CIS member states, notes the specifics of international cooperation within the CIS. Before proceeding to the presentation of one of the possible alternate for improving the CIS model law on protection of consumers, authors analyze the state of modern legal regulation in the European Union, solitary states of the Western Europe, the CIS member states. As a result, it is concluded that it is possible to take into account the achievements of foreign lawmaking in the development of regulatory decisions in a number of areas of consumer protection: extension of scope of consumer’s legislation, formulating additional guarantees for protecting consumer rights in the field of e-commerce, developing a system of cross-border consumer’s disputes, and others. The structure of the act of consumer’s legislation proposed by the authors is based on the focus of the Russian Federation on the codification of consumer’s legislation, as well as on the modern needs for a uniform complex normative legal act that conflate both general issues of regulation of consumer’s relations and special provisions relating to the protection of consumer rights in certain areas of social relations (electronic commerce, sale of food, pharmaceutical products, housing and communal services, tourism services) or consumers belonging to socially vulnerable groups of the population (minors, the elderly, the disabled).

AI 스마트 하우징 구축이 서울시 아파트 거래가격에 미치는 영향

The AI smart housing market is expected to grow, but there is no study on the effect of the installation of AI smart housing on housing prices in terms of activation. It was attempted to demonstrate whether there is a housing price as a characteristic of a house through the hedonic model, which is most commonly used.&#x0D; The empirical data was used by combining the K-apt apartment management fee disclosure data and the apartment price and apartment characteristics data provided by Real Estate 114 Reps. According to the apartment management cost information provided by K-apt, 2,144 (12.15%) of 17,638 apartment complexes nationwide for which the AI Smart Housing maintenance fee is charged as an AI Smart Housing were installed. In the case of Seoul, an AI Smart Housing was installed in 213 (8.59%) of the total 2,479 apartment complexes.&#x0D; As a result of attaching price information to the management cost information, a total of 12,073 apartment transaction price data were used, and 1,598 pieces of AI Smart Housing installation complexes were used for the analysis. As a result of the empirical analysis, as in the general hedonic model, the house sale price per unit area was higher as the transaction level increased, the number of years elapsed, the total number of households, and the common management cost per household increased. In the case of common management fees, in the case of apartments that are recently supplied, the common management fees are high according to the trend of supplying various resident convenience facilities such as swimming pools and golf driving ranges in the complex, as well as large lobby spaces, and this can be seen as a result of this part being reflected in the house sale price. . In addition, the fact that the smaller the exclusive area is, the higher the price per unit area is due to the fact that the preference for small flats due to the downsizing of the household size, such as the increase in one- or two-person households, is reflected. Lastly, it was found that the apartment price per unit area was higher when the AI Smart Housing, the subject of this study's interest, was installed, compared to the case where the AI Smart Housing was not installed.