USE OF QUANTITATIVE RATIOS FROM THE SYSTEM OF NATIONAL ACCOUNTS IN PREDICTING PRICE BUBBLES ON THE HOUSING MARKET

One of the main causes of the economic and sovereign debt crisis in 2010 – 2012 in some European countries like the United Kingdom, Spain and Ireland was the bursting of the residential market price bubble that was formed in the previous period. In this paper, a specific methodology of indicator analysis of the System of National Accounts and other data has been analyzed if it can help identify and prevent forming of some possible future price bubbles at the residential market, and therefore negative macroeconomic consequences of their bursting. Comparative indicator analysis and critical values suggest measurements of excessive construction activity that led to forming of price bubbles on the residential market. Econometric analysis has shown that it is not possible to establish critical values as variable of interest is not statistically significant.

Optimal residential load scheduling under dynamic pricing and demand-side management

Balancing electricity consumption and generation in the residential market is essential for power grids. The imbalance of power scheduling between energy supply and demand would definitely increase costs to both the energy provider and customer. This paper proposes a control function to normalize the peak cost and customer discomfort. In this work, we modify an optimization power scheduling scheme by using the inclined-block rate (IBR) and real-time price (RTP) technique to achieve a desired trade-off between electricity payment and consumer discomfort level. For discomfort, an average time delay between peak and off-peak is proposed to minimize waiting time. The simulation results present our model more practical and realistic with respect to the consumption constrained at peak hours.

An Assessment Methodology for the Evaluation of the Impacts of the COVID-19 Pandemic on the Italian Housing Market Demand

Due to the forced changes that the COVID-19 pandemic has had in many aspects of ordinary life (working, social life, training and learning in schools etc.), the characteristics of domestic spaces have significantly modified buying, selling and renting decisions in the real estate market. The aim of this research was to structure a methodology, articulated into four steps, to assess the variation that has occurred in residential market demand as a result of COVID-19 anti-contagion measures, with regard to six metropolitan Italian cities. We considered two samples of properties for each city, the first referencing the pre-COVID-19 pandemic spread and the second collected in the current situation. Consequently, we were able to determine variations in residential market appreciations through the application of a regressive econometric technique. The obtained results highlighted the relevance assumed by indoor acoustic and thermal comfort property factors, compared with the pre-COVID-19 condition. The proposed methodology could be useful to support the public and private entities involved in urban investment decision-making processes, allowing us to identify the most appreciated factors of the residential real estate market demand, in order to improve the conditions of existing and future assets and reduce the related risk levels.

Data-Driven Methodology for Coliving Spaces and Space Profiling Based on Post-Occupancy Evaluation through Digital Trail of Users

Sustainable spaces are those that are optimized, accessible, promote user experience and aim to reduce CO2 emissions while enhancing users’ well-being and comfort. The purpose of this paper is to present a methodology that was developed during the COVID-19 pandemic to understand and improve the use of coliving spaces based on remote Post-Occupancy Evaluation (POE) analysis of the digital trail generated by the users. Applying the POE methodology based on data collection from IT infrastructure enabled to identify opportunities to improve the future design of human-centered spaces. The residential market, design-wise traditional for centuries, is now facing a high-speed adaptation to the changing needs, accelerated by the COVID-19 crisis. New ways of living and shared spaces like Coliving are escalating. Technology is both an enabler of this shift in housing and the solution to operating and managing these new buildings. This paper demonstrates, through the case study of a Coliving space located in Madrid, Spain, the benefits of implementing data analysis of the digital trail collected from in-built IT systems such as smart locks, Wi-Fi networks and electric consumption devices. The conclusion is that analysing the available data from the digital infrastructure of coliving buildings can enable practitioners to improve the future design of residential spaces.

Analysing the Residential Market in Trójmiasto Using Self-Organizing Map

Although the residential property market has strong connections with various sectors, such as construction, logistics, and investment, it works through different dynamics than do other markets; thus, it can be analysed from various perspectives. Researchers and investors are mostly interested in price trends, the impact of external factors on residential property prices, and price prediction. When analysing price trends, it is beneficial to consider multidimensional data that contain attributes of residential properties, such as number of rooms, number of bathrooms, floor number, total floors, and size, as well as proximity to public transport, shops, and banks. Knowing a neighbourhood's key aspects and properties could help investors, real estate development companies, and people looking to buy or rent properties to investigate similar neighbourhoods that may have unusual price trends. In this study, the self-organizing map method was applied to residential property listings in the Trójmiasto area of Poland, where the residential market has recently been quite active. The study aims to group together neighbourhoods and subregions to find similarities between them in terms of price trends and stock. Moreover, this study presents relationships between attributes of residential properties.

Superstitions and Residential Property Buyer Decision Making in Sri Lanka

The purpose of this study is to identify the influence of superstitious beliefs on the residential property buyer’s decision-making in Sri Lanka. Despite plethora of research devoted to study superstitious beliefs affecting residential property prices, limited studies are available discussing the effects of superstitious beliefs on the entire buyer decision-making process. Besides, no studies are dedicated to discuss the issue pertaining to the Sri Lankan residential market. Rooted to Vastu, Almanac, and Islamic discipline, superstitious beliefs on the residential property exist in Sri Lanka. These superstitious beliefs, on the whole, concentrate on the design, shape, alignment, size, location, and structure of residential properties. Superstitious beliefs of buyers become a factor affecting their problem recognition, an insight for the information search, a criterion for evaluation of alternatives, a critical factor to make a purchase or purchase intention decision, and measurement of satisfaction of the residential property purchased. These findings are based on in-depth interviews with twenty (20) residential and community experts and thirty (30) residential buyers. John Dewey's five-stage buyer decision-making process is employed as a theoretical framework for data analysis. This examination provides useful insights on the behavioural aspect of the residential market in Sri Lanka for its market actors including real estate developers, agents, businesses, and real estate planners.