real estate sales
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Author(s):  
Boris Khrustalev ◽  
Ekaterina Klyueva ◽  
Sergei Zakharov

The article addresses the efficiency of construction holding companies that operate in the housing market of oneRussian region. The main trends in the development of construction holding companies in the Penza region areoutlined within the framework of the Strategy for the social and economic development of the Penza region through2035. The co-authors analyze the principal characteristics of Termodom, a Penza-based construction holdingcompany. In particular, they explore the implementation of its market strategy, the cyclical nature of the housingmarket development, cycles of construction project development and implementation, and real estate sales overthe period exceeding the last five years. Research methods include theoretical analysis and empirical researchsuch as statistical data analysis, as well as data description and grouping. The co-authors have studied workson the operation of construction enterprises, research articles, monographs, electronic resources, and legal acts.Research methods, employed by the co-authors, include description, comparison, and classification. The coauthorsuse practical approaches to the monitoring and analysis of the activities performed by various constructionholding companies with a special focus on the milestones of their strategic development and with regard forthe factors of internal and external environments. This approach has enabled the co-author to project the mainpatterns of future sustainable development of these enterprises and the construction industry as a whole.It is necessary to develop a system of legislative, regulatory and economic standards to efficiently solve the problemof unfinished construction projects, to switch over to innovative technologies and implement other innovations andproper development strategies with a focus on investments, innovations, internal and external market potential. Theproper analysis of factors of external and internal environments is a must for the successful attainment of theseobjectives.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Simen Dalland ◽  
Randi Hammervold ◽  
Henrik Tangen Karlsen ◽  
Are Oust ◽  
Ole Jakob Sønstebø

Purpose This paper aims to study aggressive bidding strategies in real estate auctions – a structural equation modelling (SEM) approach. Design/methodology/approach The authors use two data sets to study aggressive bidding strategies. First, the results from a survey with 1,803 participants examining real estate auctions are used to identify bidding strategies and related motivations. Second, the authors apply SEM by using data from 1,078 exclusive auction journals from real estate sales in Norway to study both the direct and indirect price effects of the bidding strategies. Findings The authors define four aggressive bidding strategies: high opening bid, high bid increase (jump bids), short acceptance deadline and short response time. The authors find that all four strategies yield a higher sales price. Bidders can actively influence the behaviour of the other participants and cool the potential auction fever, thus reducing the final price premium. Originality/value This paper gives households, investors and policymakers a better understanding of how bidding strategies affect real estate auctions and the final price.


Author(s):  
Hui Shi ◽  
Zhongming Ma ◽  
Dazhi Chong ◽  
Wu He

2020 ◽  
pp. 875529302094418
Author(s):  
Keith A Porter

America seems to have an earthquake investment gap, paying billions more annually on average to recover from earthquakes than it invests to prevent losses beforehand. Two large studies for Federal Emergency Management Agency (FEMA) and the US Geological Survey (USGS) offer insight into how well American buildings will resist future catastrophic earthquakes. They suggest that the public prefers new buildings to do more than to assure life safety, which has been the building code’s historic objective. They also suggest that greater resilience would better serve society’s economic interests. People expect to be safe in new buildings and the building code delivers safety. But people also want to use buildings after the Big One. America has a few options for meeting those expectations, including stronger, stiffer construction, with geographically optimized strength and stiffness. Greater strength and stiffness is not the only option to improve resilience, but such an approach offers the advantages that it could be implemented in practice by any structural designer without requiring additional technical expertise, software, of proprietary technology. It would produce a healthier economy and save society an average of $4 for every $1 of added cost. The savings cross property lines, benefiting tenants, owners, lenders, developers, and everyone who does business with them. The added cost would amount to approximately 1%, and experience in Moore, Oklahoma, shows that it would probably affect real estate sales and prices little or not at all. The solution addresses ethical considerations by responding to the public’s expectations for better performance and by optimizing utilitarian outcomes. Other options such as second-generation performance-based earthquake engineering, innovative technologies, and rating systems could complement this approach and further increase resilience.


Author(s):  
лександр Николаевич Левченков ◽  
Наталья Сергеевна Проскорякова ◽  
Ольга Сергеевна Христич

В данной статье рассматривается вопрос применения метода анализа иерархий в качестве алгоритма интеллектуального анализа данных для увеличения продаж жилья агентством недвижимости. This article discusses the application of the hierarchy analysis method as a data-mining algorithm for increasing home sales by a real estate agency.


The recent pandemic has affect economies of various countries and India is no exception. The IMF projected Indian growth rate at 1.9 percent for the financial year 2021which was previous estimated at 5.8 percent. This possess a great threat for Indian economy. This effect of COVID-19 will be felt across sectors. Indian real estate which was already recovering from the aftermath of demonetization and various reforms was jolted by this pandemic with lockdown construction activity has stopped, real estate sales are not happening. The Indian real estate should prepare itself to brace for a post COVID-19 world and should prepare itself to utilise various new and tech driven steps to come back on track. This article deals with understanding the pre pandemic real estate industry and analysing the impact of COVID-19 on Indian real estate industry. It also presents the threats and opportunities available to different real estate market participant.


Author(s):  
А.И. Кузнецов

Проведен анализ требований к обработке больших данных с использованием технологии блокчейн, показано, что в ряде проектов эти технологии, несмотря на различие архитектур, можно использовать совместно. Поставлена задача создать в компьютерной среде модель, которая показывает принцип обработки больших данных о продажах недвижимости на основе децентрализованной платформы технологии блокчейн. Определены архитектура и характеристики контрактов для микросервисов распределенного приложения. An analysis of the requirements for processing big data using blockchain technology is carried out it is shown that in a number of projects these technologies, despite the difference in architectures, can be used together. The task is to create a model in a computer environment that shows the principle of processing big data on real estate sales on the basis, of a decentralized blockchain technology platform. The architecture and characteristics of contracts for microservices of a distributed application are defined.


Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-22
Author(s):  
Mauro Castelli ◽  
Maria Dobreva ◽  
Roberto Henriques ◽  
Leonardo Vanneschi

Irregularities and frauds are frequent in the real estate market in Bulgaria due to the substantial lack of rigorous legislation. For instance, agencies frequently publish unreal or unavailable apartment listings for a cheap price, as a method to attract the attention of unaware potential new customers. For this reason, systems able to identify unreal listings and improve the transparency of listings authenticity and availability are much on demand. Recent research has highlighted that the number of days a published listing remains online can have a strong correlation with the probability of a listing being unreal. For this reason, building an accurate predictive model for the number of days a published listing will be online can be very helpful to accomplish the task of identifying fake listings. In this paper, we investigate the use of four different machine learning algorithms for this task: Lasso, Ridge, Elastic Net, and Artificial Neural Networks. The results, obtained on a vast dataset made available by the Bulgarian company Homeheed, show the appropriateness of Lasso regression.


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