Sale Price Research Articles

An investigation of the impact of energy performance certificate (EPC) ratings on residential property prices in Oxfordshire: a hedonic study

ABSTRACT This paper investigates whether energy performance ratings measured by Energy Efficiency Certificates (EPCs) affect the sales prices of residential properties in Oxfordshire, UK. The study makes use of 186,913 sales transactions that took place between 1995 and 2023. Using a hedonic regression estimation, a nonlinear pattern between energy efficiency levels and the sales prices of dwellings, are observed. The results also show that there is evidence of a significant positive relationship between EPC ratings and detached, semi-detached, terraced houses and flats. In the second part of this study, we examine the effects of age, county and type of dwelling on price premiums. To the best of the authors’ knowledge, this study is the only study, using the most comprehensive literature; and the largest, most up to date available data in analysing the Oxfordshire real-estate market, in terms of energy efficiency and price premiums.

RURAL PIG FARMERS: A CASE STUDY ON WELL-BEING PERCEPTION IN TEPETLÁN, VERACRUZ, MEXICO

Rural pig farming (Sus domesticus Erxleben) is a major agricultural activity in Veracruz, Mexico. Given the controversies surrounding pig farming, the perception of well-being was studied along with the impact of this activity in Vicente Guerrero, in Tepetlán, Veracruz, Mexico. Twenty-six pig farms were characterized to understand their dynamics and profiles. Then, seven farmers were selected to conduct a case study on the location. A triangulation was carried out with other family members, local authorities, and a former farmer in the community. The segment interviewed was all female, with an average age of 48 years. Pig farming is the second most important economic activity in households and the main source of income for women. Production systems are traditional, with pared floors; 62.9 % are carried out in a space smaller than 1000 m2. On average, each farmer has 21 animals and 53.9 m2 of pigsties, and 96.2 % of farmers do not treat the waste generated by this activity. The interviewees relate well-being to the economy and health. The pig farming business does not currently satisfy the economic needs of the farmers. To generate more income, it is required to decrease the cost of pig feed and raise the sale price. Additionally, this activity affects the well-being of other members of the community.

Towards a Better Uncertainty Quantification in Automated Valuation Models

AbstractThis study introduces a novel framework for quantifying prediction uncertainty in automated valuation models (AVMs), crucial tools in modern real estate finance. While non-linear AVMs excel in predictive performance, their limited methods for assessing prediction uncertainty reduces reliability and practical utility. We address this gap by proposing an approach for quantifying the uncertainty associated with predicted house prices and by introducing a model-specific AVM uncertainty estimate (AVMU) for AVM comparisons. Using a dataset of 51,747 historical apartment transactions in Oslo, Norway, we train three AVMs (XGBoost, random forest, support vector machine) to predict sales prices. Thereafter, we develop three base uncertainty estimators (direct loss estimation, bootstrap ensemble, quantile regression) and three meta estimators (average regressor, voting regressor, stacked generalization) for uncertainty quantification. Conformal calibration aligns the outputted uncertainty estimates from the six estimators with standard deviations of corresponding prediction errors. Having strong positive correlations with observed absolute prediction errors, the calibrated uncertainty estimators are shown to effectively capture prediction uncertainty. While the direct loss estimation excels among base estimators, the voting regressor and stacked generalization meta estimators consistently outperform it. Furthermore, by using the AVMU estimate from the stacked generalization meta estimator we can successfully identify the best-performing AVM for three separate apartment portfolios without knowing true sales prices. This alignment of the mean estimated prediction uncertainty with observed deviations underlines the utility of pre-factual AVMU estimates for model comparisons. In conclusion, our framework helps bridge prediction accuracy and uncertainty for AVMs, enhancing their reliability and supporting informed decision making for stakeholders.

Cooperation mode selection strategies in platform ecosystems: analyzing brand value, cross-selling, and platform empowerment

Purpose This paper aims to investigate the vertical cooperative relationship between the core enterprise and the manufacturer within the platform ecosystem, specifically analyzing the optimal decision-making processes of both parties under the original equipment manufacturer (OEM) and original brand manufacturer (OBM) modes. Design/methodology/approach This paper uses game theory to analyze the problem, considering factors such as brand value difference, cross-selling and platform empowerment. It constructs the game models for both OEM and OBM modes and discusses the selection strategies for the cooperation mode. Findings The results indicate that the choice of cooperation mode by the manufacturer and the core enterprise depends on the relative size of their brand values. In cases of inconsistent choices, cooperation can be improved by designing a transfer payment contract. When the brand value is constant, the product price is comprehensively affected by cross-selling revenue, price elasticity coefficient, cost coefficient of sales effort and cost coefficient of platform empowerment. The enterprise reduces the price only when the potential revenue brought by increasing product sales exceeds the marginal profit brought by increasing product pricing; otherwise, it raises the sales price. Originality/value The platform ecosystem is emerging as a future direction for business mode development. However, there is a paucity of research on the cooperation modes between manufacturers and core enterprises within the platform ecosystem.

Predicting House Prices Using Regression Model

This paper shows a study on the development of a predictive model for house prices. The model uses machine learning techniques to analyze a large dataset of of housing market data, including demographic information, real estate trends, and economic indicators. The goal of the model is to accurately predict the sale prices of a house given its features and traits. To achieve this target, the study employs feature selection and engineering methods to determine the most significant predictors of house prices [1]. The results of the model are evaluated using standard metrics, sucha as root mean squared error [RMSE] and mean absolute error [MAE]. The results show that the proposed model outperforms benchmark model and provides a reliable prediction of house prices. The model can be used by real estate professionals, policymakers, and homebuyers to gain insights into the housing market and make the right choice [3]. Keywords- Regression, prediction, house price, Machine Learning.

Pengaruh Promosi dan Harga terhadap Penjualan pada CV Bandar Sinergi Utama Kota Tangerang

The purpose of this research is to determine promotions and prices for sales at CV Bandar Sinergi Utama, both partially and simultaneously. This research is associative with a quantitative approach. Sampling was taken using the Slovin technique. The sample used in this research was 38 respondents. Data collection techniques used questionnaires, data analysis techniques using validity, reliability, hypothesis testing, t tests and f tests with SPSS version 21 software. The research results showed that: (1) Promotion has an effect on sales with a coefficient of determination value of 0.594 which has a significant effect on sales with the result tcount > ttable or (7.250 > 2.030) with a significance value<0.050 or (0.00 < 0.05). (2) There is an influence of price on sales with a coefficient of determination of 0.658 with the result tcount > ttable or ((8.319 >2.030) with a significance value <0.05 or (0.00 <0.05). (3) promotion and price has an effect on sales with the regression equation Y = -0.055 + 0.250 X1 + 0.522 < 0.05) then in this study Ha is acceptable.

Buyer Valuation of Angus Bull Attributes in Wisconsin

Abstract Hedonic analysis of Wisconsin Beef Improvement Association Bull Sale and Development Program data revealed buyer preferences for calving ease, growth, production weight, and carcass merit traits. Attributes like calving ease direct Expected Progeny Differences (EPD), average daily gain, birth to yearling gain EPD, and rib-eye area consistently ranked higher and significantly influenced the bull’s sale price. Further analysis using a 2-Class Finite Mixture Model indicated distinct groups of buyers in the region who prioritized measured bull attributes and others who did not, confirming heterogeneity in buyer preferences.

Automated pricing and replenishment forecasting for vegetable items

Firstly, the thesis extensively analysed and processed the vegetable sales data of a fresh food superstore. It integrated the product information and sales data of each vegetable category, and carried out data cleaning and analysis through Excel tools, including sorting out the outliers, listing the sales volume of each of the six categories, using pivot tables for statistical analysis, and establishing line graphs, heat maps, scatter plots, and other visual displays of the data characteristics and correlations. Secondly, the thesis investigated the relationship between sales volume and pricing, fitted the correlation between total sales volume and pricing using a random forest regression model, and predicted the daily replenishment volume for the coming week using an LSTM time series model. It also fitted quantitative fitting formulas for metrics for each vegetable category, classified profits into normal sales volume profits and discounted sales volume profits, and ultimately maximised profits through dynamic programming models and particle swarm optimisation algorithms. Finally, the thesis considered the impact of other factors such as weather, holidays, seasons and market environment on sales volume with specific data collection and analyses that help guide better pricing strategies. The findings will contribute to a better understanding of vegetable sales behaviour and optimise superstore operational strategies.

Brand Strategy And Business Performance: How Does Logo Changes Affect Sales And Stock Prices?

Branding strategy influences business performance significantly. This paper investigates how logo changes affect the sales and stock price. Previous research has focused on the importance of logo design. However, none have analyzed empirical data on actual companies that can be utilized to further interpret the real-world implications of existing restaurant brands. A quantitative, statistical analysis employing an ANOVA, Mann-Kendall, and a correlational test was conducted to determine whether a logo change had a particularly significant change, trend, and pattern on a restaurant brand’s stock price and revenue sales. Furthermore, company logos were categorized between logotypes (text) and combination marks (symbolism and text) to determine whether different types of logos have alternative effects. Results showed that a logo redesign didn’t have a significant effect on the stock price. However, there was a significant change in revenue sales after a logo change for certain companies after ~1-2 years with logotypes typically having more success.

Optimal Replenishment Strategy for a High-Tech Product Demand with Non-Instantaneous Deterioration under an Advance-Cash-Credit Payment Scheme by a Discounted Cash-Flow Analysis

This study investigated non-instantaneous deteriorating items because not all products deteriorate immediately. In the high-tech product life cycle, the product demand increases linearly substantially in the growth stage and maintains a near-constant level in the maturity stage. This is a ramp-type demand rate. To satisfy the demand as shortages occur, partial backlogging is necessary. The advance-cash-credit payment scheme, comprising advance, cash, and credit payments, has gained popularity in business transactions to improve cash flow flexibility among supply chain participants. This study explored a partial backlogging inventory model with ramp-type demand for non-instantaneous deteriorating items under generalized payment. The proposed model also incorporated discounted cash flow analysis to account for the time value of the profit function. This study attempted to determine the optimal replenishment strategy to maximize the present value of the total profit. Finally, we conducted a sensitivity analysis to examine the efficacy of the proposed model and gain managerial insights. The optimal total profit rises with an increase in the permissible delay period and sale price but decreases with an increase in ordering and purchase costs. Then, the decision-maker can refer to the managerial insights to choose the appropriate parameter value for the operation.

Budget impact analysis of pembrolizumab versus the novel PD-1 inhibitor toripalimab in locally advanced or metastatic nonsquamous non-small cell lung cancer

ABSTRACT Aim To estimate the budget impact of adding a toripalimab regimen to the existing treatment mix of pembrolizumab, both with pemetrexed and carboplatin, in patients with locally advanced or metastatic nonsquamous NSCLC within two price inputs (wholesale acquisition cost (WAC) and average sales price (ASP)). Methods Budget impact analysis comparing a treatment mix “without” versus “with” the toripalimab regimen in the annual US nonsquamous NSCLC population treated with a PD-1 inhibitor, a 3-year time horizon, toripalimab market share of 1% in 2024, increasing to 4% (2025) and 5% (2026), and medication use adjustments for discontinuation or progression to estimate fully-treated-patient-equivalents. Cost inputs included drugs, administration, and grade 3/4 adverse event (AE) management. The models were replicated in a 1-million-member plan to estimate costs per-member-per-month (PMPM) and per-member-per-year (PMPY). One-way (OWSA) and probabilistic sensitivity analyses (PSA) as well as two scenario analyses were performed. Results In the “without” scenario, the 3-year WAC costs for the pembrolizumab regimen total $40,750,234,637 ($39,024,548,745 for treatment and $1,725,685,894 for managing AEs). In the “with” scenario, these costs decline to $39,341,379,081. Corresponding “with” costs for toripalimab are $1,186,027,704 (treatment) and $99,454,471 (AE management) for a total of $1,285,482,175. This yields annual net savings of between $10,779,362 (at 1% market share) in 2024 and $64,858,298 (5% market share) in 2026, for 3-year savings of $123,373,381. The associated savings in a 1-million-member plan are $0.030 PMPM and $0.363 PMPY. The ASP model shows similar patterns. Savings were demonstrated in 68% of PSA simulations; OWSAs and scenario analyses reveal how parameter variability impacts results. Conclusion Significant savings are likely achievable from treating between 1% (year 1) to 5% (year 3) of nonsquamous NSCLC patients with the toripalimab regimen. Projected 3-year savings range from $122 million (ASP) to $123 million (WAC); corresponding to savings of $0.030 PMPM and $0.363 PMPY.

Assessing cap-and-trade regulation's impact on dual-channel green supply chains under disruption

The rise of online shopping has intensified focus on the dual-channel supply chain, highlighting the need to balance profitability and eco-friendliness under competition and disruptions. This study investigates how cap-and-trade regulations affect the performance of a dual-channel green supply chain amidst disruptions. It explores the interplay between pricing and greening strategies under cap-and-trade conditions and varying market demand disruptions. The model features a Stackelberg game between a manufacturer of eco-friendly products and a retailer, applied to both centralized and decentralized systems. Findings reveal that cap-and-trade regulations affect centralized and decentralized supply chains differently. Centralized systems show higher sensitivity in greenness levels to disruptions compared to decentralized ones. Lower greening cost coefficients lead to higher greenness levels in both models, but centralized systems outperform decentralized ones when cap-and-trade regulations are absent. However, the performance of centralized systems deteriorates significantly during severe disruptions, whereas decentralized systems maintain profitability growth. Additionally, the study finds that increased greening costs reduce the optimal greenness level, impacting wholesale and sale prices. Centralized supply chains exhibit greater variability in carbon emissions and profits under disruption conditions, while decentralized systems show more stability and higher profitability, particularly in severe disruptions. The findings also indicate that a dynamic cap-and-trade system can enhance adaptability and efficiency in managing emissions during disruptions.

Dynamic analysis of green hydrogen production integrated with storage tanks: An economical assessment for different demands

The current study aims to investigate green hydrogen production by wind and solar energy with different hydrogen storage scenarios, taking into account the economic factors. The considered renewable energy sources (RESs) are a 3 MW wind turbine and a 3.54 MW solar photovoltaic (PV) system. System modeling is performed in TRNSYS, supplying the energy required by an electrolyzer. The goal of this research is to determine an optimal economic scenario using sensitivity analysis under the same electrolyzer capacity and renewable energy generation. The calculations for various hydrogen storage scenarios are examined and evaluated for different hydrogen sale prices ranging from 3 to 15 $/kg. Hydrogen production is sized with different storage scenarios and various demands under 200 bar and 350 bar pressures and on-grid/off-grid scenarios. Results show that for a 15 $/kg hydrogen price, the optimal scenario for hydrogen demand is 350 Nm³/hr in the on-grid scenario with a pressure of 350 bar. Likewise, for the rest of the considered hydrogen sale prices, the results are presented through a comprehensive study of different approaches. The results show that for lower hydrogen unit prices, the smaller tanks will be preferred, while for higher hydrogen prices, bigger tanks are preferred. In terms of emissions, for the same amount of hydrogen production, there will be 6350 tons of CO2 emission reduction when renewable resources are used instead of fossil fuel-based hydrogen production systems.

Prescription predicament: Unveiling the financial fallout of drug shortages.

1 Background: Managing drug shortages has become a part of care delivery in oncology. Shortages have been reported to result in missed/delayed treatment, substitution of alternative therapy, increased costs, and significant administrative burden. A national shortage of cisplatin caused by manufacturing disruptions was reported by the Food and Drug Administration in the United States from February until August 2023 (shortage period). We assessed the effects of cisplatin shortage on the treatment of head and neck cancer (HNC) at practices in The US Oncology Network (The Network). Methods: Using drug administration data from medical records and claims for 26 practices in The Network prior to (July 2022 to January 2023), during, and after (September 2023 to March 2024) the shortage period, we assessed the utilization trends and financial impact (using Medicare average sale price (ASP)) of cisplatin and other anti-cancer drugs for HNC patients. Results: During the shortage period, cisplatin utilization for HNC decreased by 15% compared to pre-shortage levels. The lowest utilization occurred in June and July 2023, with a 60% decrease. Practices initially obtained supplies through an allocation model and fair-share utilization guidance from The Network Pharmacy & Therapeutics Committee. During the shortage period, alternative chemotherapy drugs for HNC saw increased use: carboplatin by 40%, paclitaxel by 24%, 5-fluorouracil by 5.3%, and cetuximab by 15%. The increased use of alternative drugs corresponded to the decrease in cisplatin usage, with new starts shifting to carboplatin (with or without paclitaxel), cetuximab and 5-fluorouracil. Ten percent of existing cisplatin recipients were shifted to an alternative drug during the shortage period. Interestingly, 5-fluorouracil was also in short supply, albeit less acutely than cisplatin. After the shortage, cisplatin volumes rebounded by 8% of pre-shortage use, while carboplatin use dropped below pre-shortage levels. Cetuximab use remained consistently 12% higher. The average cost per administration was $18 for cisplatin, $14 for carboplatin, $16 for paclitaxel, $22 for 5-fluorouracil, and $2,607 for cetuximab, based on the ASP. The increased use of cetuximab instead of cisplatin resulted in a 16% total cost increase, leading to a 144-fold increase in costs at the administration level, impacting payer costs and patient cost-sharing amounts. Conclusions: Cisplatin shortage led to a shift in utilization to alternative therapies for HNC, resulting in significant cost increases for both payers and patients. These consequences may not be limited to specific cancer types or drugs and may have other undesirable effects. Even temporary shortages of drugs can lead to lasting utilization changes and have long-term financial implications. The impacts of drug shortages on treatment decisions, guideline concordance, costs, and patient outcomes are apt for further investigation.

Dairy farm management factors associated with auction sale price of young dairy calves sold at auction markets in Québec, Canada. A cross-sectional study

Each year around 150,000 surplus calves are sold at auction markets in Québec, Canada. Surplus calves (male or female not kept in the herd of origin) are sold at a young age, but these animals are at risk of receiving lower quality neonatal care than replacement heifers. Knowledge of factors associated with a higher selling price could help convince farmers to spend more resources in the care surplus calves. Our objective was to explore the associations between farm management practices and the median percentile of surplus calf selling price per farm at auction markets. The price (CAD/kg of body weight) and the individual identification number of surplus calves sold in 2 auction markets in Québec during 4 sales days in the summer 2019 and in the winter 2020 were recorded. The recorded price of each surplus calf was transformed as percentile for each breed and day of sale. Farmers managing the surplus calves were contacted and interviewed on farm management practices. The data from farmer's interviews were analyzed as potential variables associated with the median percentile of calves' selling price per farm (farm level dependent variable) in a multivariable linear regression model. A total of 509 farmers were contacted, of which 433 farmers agreed to participate, and 409 interviews were retained for statistical analysis. The farms enrolled in the study had sold a median of 2 calves (range 1-19 calves) during the sale days considered. The main breed of surplus calves sold were Holstein (82%) and Angus crossbred calves (9%). The results from the multivariable model showed that median percentile of calves' selling price was positively associated with farms with an average milk production per cow superior to 11,000 L/years (β 0.13, 95% confidence interval (95% CI): 0.045, 0.221) and farms with 3 or more workers available to take care of surplus calves (β 0.08, 95% CI: 0.005, 0.167). Those results indicate that farms having an average milk production per cow superior to 11,000 L/year increase the calves' selling price by 13 percentiles (i.e., from 50th to 63rd) and that farms having at least 3 caretakers increase their median percentile calves' selling price by 8 percentiles (i.e., from 50th to 58th). Median percentile of calves' selling price was negatively associated with farms that vaccinated cows for neonatal calf diarrhea (β -0.06, 95% CI:-0.127, -0.011), that do not disinfect the navel of newborn calves (β -0.07, 95% CI:-0.133, -0.012), that allow transporters to enter the farm's building (β -0.07, 95% CI:-0.130, -0.015) and that used wood shaving as bedding for surplus calves (β -0.08, 95% CI:-0.156, -0.021). Sensitivity analyses performed on farms that have sold 2 or more surplus calves did not show significant changes in the associations found. Despite the fact that the study was based on self-reported questionnaire answers and a small number of calves per farm, it provides insight on farm management practices associated with median percentile of surplus calf selling price at the auction markets. Taking these results in consideration, farmers could potentially improve the market value of their animals.

Sales Promotion and Pricing Strategy in Nigerian Telecommunications Companies

Sales Promotion and Pricing Strategy in Nigerian Telecommunications Companies

Can the Phenomenon of Automobile Electrification be transformed into the Development Direction of the Automobile Industry?

Internationale Automobil-Ausstellung Show in 2023 just came to an end. In April, 2024, German Federal Chancellor Scholz led a huge business delegation to visit China (China Digital Space, 2024) Germany, which has always been proud of the automobile industry and technology, has caused a heated discussion about the development direction of the automobile industry from professional automobile media to online self-media[2]. As an international student studying advanced business management expertise in Germany, the author is curious about this changes. In particular, the latest media reports show that Apple has given up pushing forward the car-building project for nearly 10 years. Subsequently, Källenius, Chairman and CEO of Mercedes-Benz Group, also said that Mercedes-Benz would no longer adhere to the original goal of fully turning to electric vehicle sales in major markets by 2030. Previously, Mercedes-Benz's pursuit of motorization was fanatical. In 2021, Mercedes-Benz proposed an all-electric strategy. The goal is that the sales of all-electric and plug-in hybrid vehicles will reach 50% by 2025. Starting from 2030, Mercedes-Benz will be basically transform into an all-electric vehicle. These developments once again aroused the author's interest in whether motorization will become the development direction of the automobile industry. Hegel, a German philosopher, once pointed out in Science of Logic that existence is a phenomenon but it is not an illusion. Illusion corresponds more to existence, and phenomenon is higher than existence with richer connotation, because phenomenon contains its own reflection and reflection of other things, and phenomenon itself also has its own internal stability contradiction. Germany is a country of rational speculation, and the appearance of any phenomenon will not affect the Germans' thinking and exploration of its essence. Similarly, from the perspective of subjective feeling and rational analysis, this paper will try to discuss the change of German automobile consumption trends from the perspective of commercial marketing, and then prove the inherent logic of some industry behind the trend. With interest in learning and thinking, the author tried to drive a fuel-powered car and an electric car by himself. One is a German Mercedes-Benz GLC 300 manufactured and sold in China. This car is very popular among middle class consumers in China. Another model that I test drive is the M9, which was launched in the fourth quarter of 2023. It was jointly developed by Huawei, a network intelligent terminals supplier in China, Energy storage battery maker called Ningde Times and Seres Veicles. According to the report of China's mainstream media, China National Radio Network, on March 19,2024, the media once again held the top five positions of luxury brands with a weekly sales volume of 86,000 vehicles, and tried out two models. Another market factor is that the sales price of these two models are in the range of 500 thousand to 600 thousand RMB.

Ervas medicinais: Promovendo sustentabilidade e renda na agricultura familiar

The research aimed to analyze the costs and feasibility of the cultivation of medicinal herbs Melissa (Melissa officinalis) from Passion Fruit (Passiflora edulis) in a rural property in the municipality of Prudentópolis, Paraná. It is based on the premise that crop diversification is essential for the economic self-sufficiency of small properties characterized as family farming. In addition to the economic aspects, the choice of plants for analysis is intrinsically linked to the symbolic and cultural value that medicinal herbs carry, being widely recognized for their therapeutic and healing properties. The cultivation of medicinal plants, therefore, not only contributes to physical health, but also acts as a practice that values the connection with nature and ancestral knowledge, promoting a balance between the physical, emotional, and spiritual well-being of farmers and the local community. Methodologically, the research is applied, descriptive, documentary, bibliographic and based on case studies. The locus of the research was defined by convenience and accessibility. After data collection and analysis, it was found that both crops are profitable, with a financial return of approximately 30.5% per year. In addition to their economic viability, these plants offer a holistic approach to well-being, contributing to the mental and emotional balance of farmers, who, through cultivation, also rescue traditional practices and strengthen the bond with the land and local culture. The cultivation of medicinal herbs, such as Melissa and Passion Fruit, thus stands out not only as an excellent option for generating income and work, but also as a path to sustainability and the rescue of harmony between human beings and nature, presenting plant alternatives with simple management, good acceptance in the market, ease of sale and attractive prices

The impact of gunfire on residential property values

ABSTRACT Hedonic studies of housing unit prices routinely attribute lower prices to perceived undesirable attributes and or amenities of the property sold. A steady stream of literature finds that criminal behaviour is negatively capitalized into housing unit prices. This study focuses on the effects of geocoded gunfire incidents on housing unit prices while controlling for other attributes and amenities at the census-block group level. The primary finding is that spatial and temporal proximity of gunfire incidents reduces housing unit prices in an analysis of 3,844 gunfire incidents, involving over 15,000 rounds of gunfire and 2,053 housing unit sales in Savannah, Georgia, from 2015 to 2020. The effect of gunfire is greater when it occurs in the immediate vicinity of the property. Heterogeneous effects on sale prices were found to be present across time and space when intensity was measured by the number of days with gunfire incidents and to a lesser extent when intensity was measured by the number of shots fired in each incident.

Pricing strategies in the Silicon Valley housing market: an update on TOM and recent events

Purpose 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. Design/methodology/approach 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. Findings 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. Practical implications 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. Originality/value 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.