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This study aims to explore the relationship between house prices and time-on-market (TOM) in Silicon Valley. Previous findings have been inconclusive due to variations in property characteristics. This paper highlights the discrepancy between listing and selling prices and identifies differences among housing types such as condominiums, detached houses and townhouses based on housing orientations and customer groups. Additionally, this study considers the impact of the COVID-19 pandemic and the Fed’s interest rate policies on the housing market.

The authors analyze 63,853 transactions from the Bay East Board of Realtors’ Multiple Listing Service during 2018 to 2022. The study uses a multiple-stage methodology, including a nonlinear hedonic pricing model, search theory and two-stage least squares method to address concerns relating to endogeneity.

The Silicon Valley housing market shows resilience, with low-end properties giving buyers more bargaining power without significant price drops. High-end properties, on the other hand, attract more attention over time, leading to aggressive bidding and higher final sale prices. The pandemic, despite reducing housing supply, did not dampen demand, leading to price surges. Post-COVID, price correlations with TOM changed, indicating a more cautious buyer approach toward high premiums. The Fed’s stringent monetary policies post-2022 intensified these effects, with longer listing times leading to greater price disparities due to financial pressures on buyers and shifting dynamics in buyer interest.

Results reveal a nonlinear positive correlation between TOM and the price formation process, indicating that the longer a listed property is on the market, the greater the price changes. For low-end properties, TOM becomes significantly negative, while for high-end properties, the coefficient becomes significantly positive, with effects and magnitudes varying by type of dwelling. Moreover, external environmental factors, especially those leading to financial strain, can significantly impact the housing market.

The experience of Silicon Valley is valuable for cities using it as a development model. The demand for talent in the tech industry will stimulate the housing market, especially as the housing supply will not improve in the short term. It is important for government entities to plan for this proactively.

This study aims to develop a database of existing academic information in house purchase decision (HPD) using systematic literature review (SLR), to facilitate worldwide advancement of research under HPD domain.

This research examined papers from two reputable databases – Scopus and Google Scholar – from 1992 to 2022 using a scoping review technique (Arksey and O’Malley, 2005) and a theme analysis method. Out of 374, 181 articles fit the inclusion parameters and were evaluated using the theme analysis approach.

Data from 181 articles was evaluated thematically to create a thematic map of HPD research. Five main themes and their sub-themes were identified: consumer behaviour, housing attributes, factors influencing purchasing decisions, investment analysis and demographics, which proved essential in understanding HPD and customer preferences for house purchase.

Data from 181 articles were evaluated thematically to create a thematic map of HPD research. This SLR intends to provide useful new insights on consumer concerns about home purchases in the rapidly developing residential real estate market and the issues that marketers, housing sector stakeholders, real estate industry and existing and future researchers should prioritize.

This research is unique such that it is the only 30-year-long SLR on the subject matter of HPD. This paper makes a significant contribution to residential real estate domain signifying the present state of research in HPD.

Literature has already analysed the relation between a property’s time on market (TOM) and other housing characteristics, but few to none include the property’s energy performance certificates (EPC) and none make a comparison between the selling and rental market. This paper aims to address these gaps by studying the relationship between TOM, price and EPC in both markets.

By introducing a combination of alternative tests, this study confirms a causal relation between TOM and price in the cross-sectional data. This allows this study to use a two-stage least square model and analyse 392,498 Flemish sale and rental properties transacted between 2019 and 2023.

The results indicate that both sale and rental properties with higher prices increase the TOM by 4–6 days, and this effect is even stronger in the selling market when the value-added tax is included. This study also finds that EPC labels have a complex relation with the time on market. A-labelled properties tend to increase the transaction time between 10 and 54 days, but B- and C-labelled properties decrease TOM between 20 and 30 days. In addition, the poorer performing labels (E and F) react differently across markets because of market-specific policies.

This paper provides novel insights by studying the relationship between TOM and EPC while also considering TOM’s endogenous relationship with the price. We control for these relationships in both the selling and rental market.

The purpose of this paper is to evaluate the impact of beneficial externality generated by the World Heritage List (WHL) on residential property values in order to offer new insights into heritage discourses.

The study uses the hedonic price model to estimate empirically the difference in prices for residential properties located in the Old Rauma World Heritage. The study uses residential sales transaction data from the City of Rauma from January 2005 to September 2012 drawn from an online database called KVKL Hintaseurantapalvelu managed by the Central Federation of Finnish Real Estate Agencies.

The research results indicate a positive, but insignificant, relationship between the property sale prices (euros/sqm) and heritage designation. However, the total sale prices are higher in Old Rauma as the properties are significantly larger in Old Rauma compared to other properties in Rauma.

Studies in heritage economics have assessed the influence of the property market on heritage listing and designation at either the national level, the local level or a mix of national/local levels. This paper contributes to the literature by analysing the impact of a United Nations Educational, Scientific and Cultural Organisation (UNESCO) world heritage designation on residential property values. UNESCO is the leading global institution which deals with the protection of heritage sites that transcend national and local boundaries.

The real estate industry has rapidly changed due to technological advances across residential and commercial real estate from the perspective of occupiers, investors, and service providers. Owners and buyers of properties have access to increasing information in the marketplace, including access to residential real estate platforms such as Zillow. Automated appraisals and artificial intelligence (AI) in the mortgage application process speed up home buying. Commercial real estate uses fintech to source deals, perform due diligence, and execute property management requests. This chapter includes a practitioner's view of the current and future information data needs, processes, and point solutions in the evolving technology landscape, including how tools such as ChatGPT apply. It concludes that the real estate fintech revolution has only begun, as data gaps in the real estate market require resolution before yielding better process automation and as the business model of real estate service providers shifts to strategic advisory roles.

The purpose of this study is to use a lifetime income measure to evaluate the long-run housing affordability for an understudied cohort of households in the literature – the millennials. The authors do this in the context of Malaysia, measuring long-run affordability for four housing types across geographic locations and income distributions.

This study calculates a long-run housing affordability index (HAI) using data on house prices and household incomes. Essentially a ratio of predicted lifetime incomes to house prices, the HAI is computed for four common housing types in Malaysia from 2005 to 2016 and for six states in the country. The HAI is also compared across four income percentiles.

The analysis reveals varying patterns of housing affordability among different states in Malaysia. Housing affordability has declined since 2010, with most housing types being unaffordable for millennial-led households with the lowest income. Housing is most affordable for those in the highest income bracket, although even here, there are pockets of unaffordable housing as well.

Based on the findings, this study proposes three targeted interventions to improve housing affordability for Malaysian millennials.

This study fills a gap in the literature by examining the long-run housing affordability of Malaysian millennial-led households based on both geographic location and income distribution. The millennial population is understudied in the housing affordability literature, making this study a valuable contribution to the field.

This paper aims to address the challenge raised in the literature regarding whether and how digitalization supports a servitized new product development (NPD) process, considering the customer’s involvement from the early stage of the process.

Pragmatic constructivism (PC) has been adopted for conceptualizing the NPD process as the construction of a new reality. PC is the method theory used for interpreting the field evidence drawn from a qualitative case study carried out at a multinational company operating in the semiconductor industry.

This study shows how digitalization supports the alignment to the overarching topoi of the company servitization strategy by enabling the integration and merging of different organizational topoi during the NPD process.

This study is confined to a single-case study and context.

The results of this study are relevant for managers involved in the stage-gate product development of manufacturing companies, informing them on how the use of digital tools enables or hinders the progression of product development projects.

This paper contributes to the servitization literature by offering field evidence that demonstrates the importance for manufacturing firms of acquiring customer feedback from an early NPD phase. Another contribution is related to the literature on the role of digitalization in NPD processes, describing how digital tools give support during the different phases of the NPD process.

Short-term rentals (STRs) (like Airbnb) are reshaping social behaviour, notably in gastronomy, altering how people dine while travelling. This study delves into revenue management, examining the impact of seasonality and dining options near guests’ Airbnb. Machine Learning analysis of Airbnb data suggests owners enhance revenue strategies by adjusting prices seasonally, taking nearby food amenities into account.

This study analysed 220 Airbnb establishments from Madrid, Spain, using consistent monthly price data from Seetransparent and environment variables from MapInfo GIS. The Machine Learning algorithm calculated average prices, determined seasonal prices, applied factor analysis to categorise months and used cluster analysis to identify tourism-dwelling typologies with similar seasonal behaviour, considering nearby supermarkets/restaurants by factors such as proximity and availability of food options.

The findings reveal seasonal variations in three groups, using Machine Learning to improve revenue management: Group 1 has strong autumn-winter patterns and fewer restaurants; Group 2 shows higher spring seasonality, likely catering to tourists, and has more restaurants, while Group 3 has year-round stability, fewer supermarkets and active shops, potentially affecting local restaurant dynamics. Food establishments in these groups may need to adapt their strategies accordingly to capitalise on these seasonal trends.

Current literature lacks information on how seasonality, rental housing and proximity to amenities are interconnected. The originality of this study is to fill this gap by enhancing the STR price predictive model through a Machine Learning study. By examining seasonal trends, rental housing dynamics, and the proximity of supermarkets and restaurants to STR properties, the research enhances our understanding and predictions of STR price fluctuations, particularly in relation to the availability and demand for food options.

This paper aims to discuss recent criticism related to partial least squares structural equation modeling (PLS-SEM).

Using a combination of literature reviews, empirical examples, and simulation evidence, this research demonstrates that critical accounts of PLS-SEM paint an overly negative picture of PLS-SEM’s capabilities.

Criticisms of PLS-SEM often generalize from boundary conditions with little practical relevance to the method’s general performance, and disregard the metrics and analyses (e.g., Type I error assessment) that are important when assessing the method’s efficacy.

We believe the alleged “fallacies” and “untold facts” have already been addressed in prior research and that the discussion should shift toward constructive avenues by exploring future research areas that are relevant to PLS-SEM applications.

All statistical methods, including PLS-SEM, have strengths and weaknesses. Researchers need to consider established guidelines and recent advancements when using the method, especially given the fast pace of developments in the field.

This research addresses criticisms of PLS-SEM and offers researchers, reviewers, and journal editors a more constructive view of its capabilities.

This study illustrates the economic and financial dynamics of the sector, analysing the evolution of the main ratios of profitability and financial structure of 1,559 Italian real estate companies divided into the three macro-regions: North, Centre and South, in the period 2011–2020. In this way, it is also possible to verify the responsiveness to the 2020 pandemic crisis.

The analysis uses descriptive statistics tools and the ANOVA method of analysis of variance, supplemented by the Tukey–Kramer test, to identify significant differences between the three Italian macro-regions.

The study shows the increase in profitability after the 2008 crisis, despite its reverberation in the years 2012–2013. The financial structure of companies improved almost everywhere. The pandemic had modest effects on performance.

In the future, other indices should be considered to gain a more comprehensive view. This is a quantitative study based on financial statements data that neglects other important economic and social factors.

Public policies could use this study for better interventions to support the sector. In addition, internal management can compare their company's performance with the industry average to identify possible improvements.

The research analyses an economic field that employs a large number of people, especially when considering the construction and real estate services covered by this analysis.

The study contributes to the literature by providing a quantitative analysis of industry dynamics, with comparative information that can be deduced from financial statements over the years.