Real Decision Research Articles

Leveraging Mobile Net and Yolo Algorithm for Enhanced Perception in Autonomous Driving

This project presents a cutting-edge autonomous navigation system designed to enhance spatial understanding and object recognition for vehicles. Equipped with cameras on both the left and right sides, by capturing comprehensive view of the surroundings, utilizing stereo vision for crucial depth information. The integration of Mobile Net and YOLO (You Only Look Once) algorithms is central to achieving real-time object detection and recognition. Mobile Net is employed for efficient feature extraction, ensuring optimal computational efficiency. Simultaneously, YOLO plays a pivotal role in rapid and accurate identification of objects within the captured images, contributing to the robustness of spatial understanding crucial for autonomous navigation. The result is a comprehensive and reliable autonomous navigation system, showcasing the effectiveness of combining cutting-edge technologies for improved real time decision making.

부동산 크라우드펀딩 특성으로 인한 투자참여의사가 활성화 방안에 미치는 연구

With the development of IT technology, crowdfunding is growing as a new financing method that connects demanders and suppliers online through the Internet. In addition, efforts to increase assets through investment have become very important, making it inevitable to expand investment in financial investment products that seek higher returns while managing risks to some extent. As a result, loan-type crowdfunding (P2P) is emerging as a new investment alternative.The purpose of this study is to study real estate investment decisions using real estate crowdfunding, and to suggest a direction to expand and revitalize the investment participation of the general public through crowdfunding. As a result of the analysis, investor characteristics, real estate market characteristics, investment product characteristics, and platform characteristics were found in the order, and the higher the importance, the higher the impact on real estate investment decision-making. This served as the basis for being in the spotlight as a new financial product, and in conclusion, first, it is the need for systematic and continuous financial and investment education from the perspective of investors. Second, in terms of platforms, P2P loan brokers must ensure transparency and safety of funds. Third, in terms of investment products, it is necessary to develop financial products, establish marketing and education strategies, and improve the quality of financial counseling services. Fourth, in terms of the real estate market, if the market such as policy and interest rates stabilizes and the real estate market is activated, it will be closer to each other’s goals not only in investment decisions through crowdfunding but also in revitalizing real estate investment participation.

Combinative distance-based assessment method using linguistic T-spherical fuzzy aggregation operators and its application to multi-attribute group decision-making

Clean energy has become a hot topic worldwide because of the continuous rise in the climate's temperature. Reduced air pollution is only one of the many economic and environmental advantages of clean energy. An effective contractor with experience and knowledge in clean energy projects is always required to handle the complicated process of planning, implementing, and maintaining the new clean energy infrastructure. Contractor selection for renewable energy projects (REPs) is a multi-attribute group decision-making (MAGDM) problem. Linguistic T-spherical fuzzy set (LT-SFS) can be compatible with fuzzier information, and the contractor selection problem also has a lot of fuzzy information, which makes LT-SFS very suitable for contractor selection for REPs. Hence, the present work proposes a MAGDM methodology utilizing a combinative distance-based assessment (CODAS) method within the LT-SF context. For that, the Einstein and Frank t-norm and t-conorm has been used to define new and generalized operational laws for linguistic T-spherical fuzzy numbers. Then, the linguistic T-spherical fuzzy Einstein and linguistic T-spherical fuzzy Frank aggregation operators are proposed to integrate the information data provided by experts. Moreover, the proposed aggregation operators based-CODAS approach is designed to evaluate and rank the available alternatives. A real-life decision problem of selecting the best suitable contractor for a renewable energy project is solved to verify our suggested technique. Moreover, the sensitivity analysis is also carried out by changing the parameter's value to check the consistency of the rank order. Finally, the new model is compared with existing approaches to demonstrate its strength. The comparative analysis shows that the results of the suggested technique are more feasible and beneficial than those of existing approaches.

Consumer purchase intention for extrinsic and intrinsic cues of construction and demolition waste recycled products

PurposeThe preferences and adoption of recycled materials by consumers are subject to a variety of factors, such as enablers and barriers. Despite this, there exists a paucity of research concerning stakeholders' perceived value and real purchase decision towards recycled products. Consequently, this research study aims to fill this gap by investigating stakeholders' perceived value of recycled products derived from construction and demolition (C&D) waste and its effect on purchase decisions.Design/methodology/approachResearch data were collected from 219 valid questionnaires completed by Chinese stakeholders. Structural equation modeling (SEM) was then employed to test eight hypotheses.FindingsThe results show intrinsic cue (materials) and extrinsic cue (brand) influence the stakeholders’ judgment on C&D waste recycled products’ value and then their purchase intention. However, cues such as quality, word-of-mouth, price, policy and advertised have not play a significant role in practice.Originality/valueThis research study verified the significance of brand and material cues on decision making for purchasing C&D waste recycled products, providing new insights to policy making to enhance the uptake of C&D waste recycled products in construction industry.

Intolerance of uncertainty predicts indecisiveness and safety behavior in real-life decision making: Results from an experience sampling study

In decision making, intolerance of uncertainty (IU) may be linked to safety behavior, which could ironically maintain IU and, thus, foster indecisiveness. In a smartphone-based experience sampling study, 247 participants described their real-life decisions six times per day and rated (a) their situational indecisiveness, (b) decision characteristics, (c) situational IU, and (d) problematic safety behaviors. Participants higher in dispositional IU reported more indecisiveness and engaged in more problematic safety behavior across measurements. The same relationships were observed with situational IU at the level of individual measurements. Engaging in more problematic safety behaviors during the first days predicted indecisiveness on the last day, mediated by IU. The results demonstrate the real-life relevance of the relationship between IU, indecisiveness, and safety behavior.

Decoding AI’s Nudge: A Unified Framework to Predict Human Behavior in AI-Assisted Decision Making

With the rapid development of AI-based decision aids, different forms of AI assistance have been increasingly integrated into the human decision making processes. To best support humans in decision making, it is essential to quantitatively understand how diverse forms of AI assistance influence humans' decision making behavior. To this end, much of the current research focuses on the end-to-end prediction of human behavior using ``black-box'' models, often lacking interpretations of the nuanced ways in which AI assistance impacts the human decision making process. Meanwhile, methods that prioritize the interpretability of human behavior predictions are often tailored for one specific form of AI assistance, making adaptations to other forms of assistance difficult. In this paper, we propose a computational framework that can provide an interpretable characterization of the influence of different forms of AI assistance on decision makers in AI-assisted decision making. By conceptualizing AI assistance as the ``nudge'' in human decision making processes, our approach centers around modelling how different forms of AI assistance modify humans' strategy in weighing different information in making their decisions. Evaluations on behavior data collected from real human decision makers show that the proposed framework outperforms various baselines in accurately predicting human behavior in AI-assisted decision making. Based on the proposed framework, we further provide insights into how individuals with different cognitive styles are nudged by AI assistance differently.

NEW CHALLENGES OF DISTANCE TEACHING AT THE INTERNATIONAL MEDICAL FACULTY DURING THE WAR AND CORONAVIRUS PANDEMIC IN UKRAINE

The aim of this work is to address specific challenges related to the organization and future prospects of distance teaching in medical higher education institutions, especially pertinent amidst the current military situation in Ukraine. Materials and methods. In our analysis, we reviewed relevant experiences and documentation, considering the participation of medical students in higher education. The data analysis on student’s performance and engagement in online courses to assess the effectiveness of current distance teaching methods was performed. Results and discussion. The education of medical professionals at international faculties holds significant national importance, contributing to addressing various societal challenges and enhancing Ukraine's educational reputation. However, recent events such as the COVID-19 pandemic and martial law have posed new challenges for distance teaching practices in pedagogy. Ii is crucial for educators who work with international medical students to possess high levels of professionalism and moral integrity. Across the globe, simulation technologies are increasingly employed in medical training, offering innovative methods for preparing highly skilled medical personnel. One such technology is the "virtual patient," which allows learners to engage in realistic clinical scenarios, make diagnostic and treatment decisions, and understand the consequences of their actions without compromising patient safety. The development of communication and language skills among future medical professionals remains a pressing concern within higher education institutions. Utilizing technological storytelling methods to illustrate the journeys of successful specialists can provide valuable insights and inspiration for students. In the context of distance learning, maintaining prompt feedback channels between students and departmental teachers is essential for effective learning outcomes. Conclusion. Practical application of innovative methodological approaches provide with opportunity for teachers of medical higher education institutions to implement and improve the novel methods of work, to increase the effectiveness of the educational process and the level of knowledge of students of the international faculty. Innovative distance learning technologies stimulate the teacher's work, increase his/her professional level, since the organization of the educational process requires preparation for each class, constant pedagogical research, development and use of additional new materials, study of innovative technologies.

Evaluation of micromobility risk management alternatives using interval-valued q-rung orthopair fuzzy interaction operators-based WISP method

Micro-mobility has become a mode of transportation since the 2010 s. Because this mode is relatively new, there are many unknowns, and the associated risks must be evaluated so they can be managed safely and effectively. This paper aims to introduce a decision support system (DSS) for ranking risk management alternatives in the micro-mobility sector under an interval-valued q-rung orthopair fuzzy (IVq-ROF) context. In this context, firstly IVq-ROF-interaction aggregation operators (AOs) are introduced for aggregating the information from IVq-ROF perspective. These AOs can provide a flexible mode to tackle realistic decision support problems within the IVq-ROF set (IVq-ROFS) context. Second, a definition of entropy and new entropies for IVq-ROFS is introduced. Third, a hybrid weight-determining formula is presented using an entropy-based weighting approach for the objective weight of criteria and a stepwise weight assessment ratio analysis (SWARA) method for the subjective weight of criteria. Fourth, the weighted integrated sum-product (WISP) approach is expanded from the IVq-ROF perspective. Further, the developed model is utilized in a case study of risk management alternative evaluation in the micro-mobility sector, which shows its applicability and feasibility under the IVq-ROF environment. Sensitivity and comparative investigations are studied to confirm the strength and stability of the obtained result. The best alternative has emerged as the state-private sector cooperation in successfully allocating the risk in the micro-mobility sector. The final results conclude that the developed methodology can provide more accurate decisions while considering multiple inducing indicators and input uncertainties.

Political motives of excess leverage in state firms

This study explores the political motives behind the enormous indebtedness of Chinese state firms. To measure the excess leverage of state firms, the difference between actual and predicted values, we balance the samples of private and state firms by matching and using the former sample to generate an out-of-sample prediction for the latter. Our results suggest that the excessive indebtedness of state firms positively relates to regional unemployment pressure and economic pressure faced by municipal politicians. These effects are more pronounced in local state firms and when local officials have stronger promotion incentives. Our paper provides evidence that government control leads to significant political influence over the real decisions of firms.

Entropy for q-rung linear diophantine fuzzy hypersoft set with its application in MADM

A notable advancement in fuzzy set theory is the q-rung linear diophantine fuzzy set. The soft set theory was expanded into the hypersoft set theory. By combining both the q-rung linear diophantine fuzzy set and hypersoft set, this study describes the notion of q-rung linear diophantine fuzzy hypersoft set that can handle multi sub-attributed q-rung linear diophantine fuzzy situations in the real world. Furthermore, some of its algebraic operations such as union, intersection and complement are described in this study. In addtion, the entropy measure of the q-rung linear diophantine fuzzy hypersoft set is established as it is helpful in determining the degree of fuzziness of q-rung linear diophantine fuzzy hypersoft sets. A multi-attribute decision making algorithm based on suggested entropy is presented in this study along with a numerical example of selecting a suitable wastewater treatment technology to demonstrate the effectiveness of the proposed algorithm in real-life situations. A comparative study was undertaken that describes the validity, robustness and superiority of the proposed algorithm and notions by discussing the advantages and drawbacks of existing theories and algorithms. Overall, this study describes a novel fuzzy extension that prevails over the existing ones and contributes to the real world with a valid real-life multi-attribute decision making algorithm that can cover many real-world problems that are unable to be addressed by the existing methodology.

What happens to information, situational cues, and individual strategies in decision-making? The contribution of latent decisional profiles in realistic decisions

What happens to information, situational cues, and individual strategies in decision-making? The contribution of latent decisional profiles in realistic decisions

Endowment Effect, Information Asymmetry, and Real Estate Market Decisions: Willingness to Pay and Willingness to Accept Disparities

Abstract The endowment effect has gained dominance over the decades due to its ability to explain behavioral instincts portrayed by individuals when making decisions and its inconsistency with standard economic theories. It has been extensively applied in different fields of study, however its applicability to the housing market has been limited since its establishment. The study seeks to investigate whether the endowment effect exists in the housing market and whether the disclosure of information, increased affordability and agent evaluation significantly reduce the endowment effect. Using data obtained from a survey conducted between January and August 2022 in the Eastern Cape Province of South Africa, the study found that the disclosure of information eliminates the gap between buyers’ valuations and market values, while the gap between sellers’ valuations and market prices is reduced but not eliminated by information disclosure. The study has further concluded that increased affordability and agent evaluations significantly reduce the endowment effect in the housing market. Therefore, the study recommends that private and public entities should establish laws and regulations which promote the disclosure of information to reduce the gap between the willingness to pay (WTP) and the willingness to accept (WTA) that exists in the market.

Examining the impact of website layout and dark triad approach on real estate purchase decisions in India: a young adult socialization mediated model

Purpose This study aims to determine website quality, young adult socialization and dark triad personality as the factors influencing the real estate purchase decision. In addition, this study also measures the mediating effects of young adult socialization on real estate purchase buying behavior. Design/methodology/approach Related literature, quantifiable variables with a five-point Likert scale, hypothesis testing and mediators are used to study the model. A systematic questionnaire that was divided into four sections was used. A total of 336 valid responses were collected and analyzed through a structural equation model. Findings The results suggest that dark triad personality and young adult socialization considerably affect real estate purchase decisions. The development proves website quality does not significantly impact real estate purchase behavior. Research limitations/implications This study is limited to a few young consumers’ responses. Future studies could be more widespread globally and should include more variables and offline methods of purchasing behavior. Originality/value As per the review of existing literature, this research is the first, to the best of the authors’ knowledge, to determine the factors affecting the real estate purchase decision with factors like website quality, dark triad personalities and young adult socialization involving it.

Convolutional Neural Networks Guided Raman Spectroscopy as a Process Analytical Technology (PAT) Tool for Monitoring and Simultaneous Prediction of Monoclonal Antibody Charge Variants.

Charge related heterogeneities of monoclonal antibody (mAb) based therapeutic products are increasingly being considered as a critical quality attribute (CQA). They are typically estimated using analytical cation exchange chromatography (CEX), which is time consuming and not suitable for real time control. Raman spectroscopy coupled with artificial intelligence (AI) tools offers an opportunity for real time monitoring and control of charge variants. We present a process analytical technology (PAT) tool for on-line and real-time charge variant determination during process scale CEX based on Raman spectroscopy employing machine learning techniques. Raman spectra are collected from a reference library of samples with distribution of acidic, main, and basic species from 0-100% in a mAb concentration range of 0-20g/L generated from process-scale CEX. The performance of different machine learning techniques for spectral processing is compared for predicting different charge variant species. A convolutional neural network (CNN) based model was successfully calibrated for quantification of acidic species, main species, basic species, and total protein concentration with R2 values of 0.94, 0.99, 0.96 and 0.99, respectively, and the Root Mean Squared Error (RMSE) of 0.1846, 0.1627, and 0.1029g/L, respectively, and 0.2483g/L for the total protein concentration. We demonstrate that Raman spectroscopy combined with AI-ML frameworks can deliver rapid and accurate determination of product related impurities. This approach can be used for real time CEX pooling decisions in mAb production processes, thus enabling consistent charge variant profiles to be achieved.

Reporting bias and feedback effect

AbstractStock prices often provide firm managers with new information that can be used in real decisions. Studies generally focus on the ex ante disclosure policy and show that the presence of market feedback crowds out firms' disclosure. We instead examine a manager's ex post biasing incentives and find that market feedback amplifies overreporting bias, but not necessarily underreporting bias, due to three interacting effects. First, the manager biases the report more with feedback since decreased information quality crowds in the speculator's private information acquisition and improves investment efficiency, regardless of the reporting scenario (the information rationing effect). Second, the manager biases more in the overreporting scenario and less in the underreporting scenario, because reporting more favorable information crowds in private information acquisition, as the speculator expects a higher subsequent investment and therefore higher trading profits (the investment scale effect). Third, market feedback influences reporting bias not only through the speculator's information acquisition but also directly through the market maker's pricing function. Specifically, the market maker decreases the price discount, as he expects that the manager may learn correct information from the price and may invest more efficiently. Expecting a lower price penalty in the presence of feedback, the manager biases more in the overreporting scenario and less in the underreporting scenario (the investment correction effect). Overall, our results suggest that granting firms reporting discretion could improve investment efficiency and firm value when managers can learn through price.

Being Well-in with the Joneses? A Lab-in-the-Field Experiment on Conspicuous Consumption among Rural Communities

Conspicuous consumption, specialised consumption of high visibility but without apparent economic benefit, is reducing investment in productive assets and thus hindering economic development in low-income countries. In previous research, the phenomenon was commonly explained by status-seeking and herding behaviour. Our study follows a novel angle in testing the role of risk sharing, assuming that investment into social status is perceived to increase access to informal credits in times of crises. We conduct a random-effects hurdle model along a lab-in-the-field experiment along a sample of 197 wheat farmers in Uzbekistan, a country characterised by high levels of risk and uncertainty. Within our experimental setup, both risk attitude and real-life risk management decision are found to be significant determinants of conspicuous consumption. Our findings support the notion of a complex decision-making process with risk sharing as one important motivator. Providing first empirical evidence on the topic, our findings have implications beyond our narrowly defined study case: We argue that strengthening options of formal risk-sharing tools might remove one of the motivators for conspicuous consumption; thus, it could improve the economic welfare of low-income households worldwide by allowing for more productive investment of scarce financial resources.

What are tenants demanding the most? A machine learning approach for the prediction of time on market

PurposeThis is the first article to apply a machine learning approach to the analysis of time on market on real estate markets.Design/methodology/approachThe random survival forest approach is introduced to the real estate market. The most important predictors of time on market are revealed and it is analyzed how the survival probability of residential rental apartments responds to these major characteristics.FindingsResults show that price, living area, construction year, year of listing and the distances to the next hairdresser, bakery and city center have the greatest impact on the marketing time of residential apartments. The time on market for an apartment in Munich is lowest at a price of 750 € per month, an area of 60 m2, built in 1985 and is in a range of 200–400 meters from the important amenities.Practical implicationsThe findings might be interesting for private and institutional investors to derive real estate investment decisions and implications for portfolio management strategies and ultimately to minimize cash-flow failure.Originality/valueAlthough machine learning algorithms have been applied frequently on the real estate market for the analysis of prices, its application for examining time on market is completely novel. This is the first paper to apply a machine learning approach to survival analysis on the real estate market.

Moderating the role of trust toward intention to adopt cloud computing by academic libraries in North-Eastern Nigerian

Cloud computing has since been recognized as a robust technology that speeds and simplifies organizational service delivery. But its adoption especially in African countries and academic libraries is still at the nascent stage. This study aims to find the contributing determinants of cloud computing adoption in North-Eastern Nigerian academic libraries using Technological and Organizational variables of Technology-Organization-Environment theory(TOE) and Diffusion of Innovation Theory (DOI). A quantitative approach using a survey research design was employed. Seventy-eight academic libraries in North-Eastern Nigeria formed the population of the study and census enumeration is used which indicates that all the academic libraries are considered. Results revealed that five out of six determinants are significant for the adoption of cloud computing in academic libraries. While moderation effect results revealed that only one determinant is moderated by the adopted moderator. This implies that the significant determinants should be given more consideration when deciding to adopt cloud computing. Equally, the study’s results will guide academic libraries’ decision-makers that include the Chief Librarian, Deputy Librarian and Head of ICT unit who are responsible for deciding on the rejection or accepting innovative technology in the library to make fruitful and realistic decisions when deciding to adopt cloud computing. Theoretically, the results will contribute to the body of knowledge especially in the librarianship field. Finally, the study recommends among others that special attention should be given to those significant determinants when deciding to adopt cloud computing in academic libraries. Future studies should employ more variables and use another factor as a moderator. Equally future studies should cover more areas beyond North-Eastern Nigeria.

Abstract B042: Analysis of predictive indicators of invasive breast cancer: Modeling survival rates

Abstract Objective: We aim to investigate indicators of risk for invasive breast cancer (IBC) and to predict and assess the impact of these risk indicators on survival rates in breast cancer (BC) patients who received various therapies. Background: It has been identified that several indicators including BC subtypes, age at diagnosis, history of breast disease, menopausal status, estrogen receptor (ER) status, progesterone receptor (PR) status, human epidermal growth receptor (HER2) status, tumor stage, tumor size, among others, can cause genomic alterations and driver mutations in BC, leading to invasive carcinogenesis. To assure improved BC outcomes, we investigate the influence of BC indicators on survival rates in patients receiving hormone therapy, chemotherapy and radiotherapy. Methods: This study uses the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset, which included 31 clinical indicators, m-RNA levels z-score for 331 genes, and mutation in 175 genes for 1904 BC patients. Both bivariate and multivariate associations were examined using logistic regression and Cox proportional hazard models. The multivariate logistic regression analysis is used to determine predictors of IBC. The Cox proportional hazard model was also used to evaluate the magnitude of the impacts of clinical and demographic variables on IBC, as well as the effects of therapy on survival. All analyses for this investigation were carried out using SAS. A p-value < 0.05 was considered statistically significant. Results: We examined 1,043 patients (with the median age 61±13.1 years) from a total of 1904 for this study: 78.8% of patients had breast invasive ductal carcinoma; 87.4% were HER2 negative; 22.8%, 60.7%, and 67.6% received chemotherapy, hormone therapy, and radiotherapy treatments, respectively; 56.9% died but only 34.6% died from the disease; the average overall survival months was 127.3±77.6 months; and the most common age group was "65+ years" (39.4%). Chemotherapy (p=0.0474), neoplasm histologic grade (p=0.0015), Nottingham prognostic index (p<.0001), radiation (p=0.0068), and BReast CAncer gene 1 (BRCA1) (p=0.0251) were all associated with IBC. Overall survival was also correlated to age at diagnosis (p=0.0002), neoplasm histologic grade (p<.0001), lymph nodes (p<.0001), mutation count (p<.0001), Nottingham prognostic index (p<.0001), radiotherapy (p=0005), tumor size (p<.0001), tumor stage (p<.0001), BRCA1 (p=0.250), PTEN (p<.0001), and TP53 (p=0.0248). The overall accuracy of the model for each IBC and survival status was 82.1% and 74.2%, respectively. Conclusion: Our analysis demonstrated the importance of predictive analysis using the logistic regression model and survival analysis using the Cox proportional hazard model in cancer research. Predictive and survival analysis results aided in predicting the invasiveness and survival rate of BC among the study participants. In general, predictive and survival analyses are more accurate and may be used to make more realistic treatment decisions and precision oncology. Citation Format: Olumide Arigbede, Gebre-Egziabhe Kiros. Analysis of predictive indicators of invasive breast cancer: Modeling survival rates [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr B042.

The price of clean air – quantifying air pollution exposure in real estate decisions

PurposeThe purpose of this paper is to explore the significance impact of air quality as a contributing factor on residential property rents by applying geo-informatics to economic issues. Since air pollution poses a severe health threat, city residents should have a right to know about the (invisible) hazards they are exposed to.Design/methodology/approachWithin spatial-temporal modeling of air pollutants in Berlin, Germany, three interpolation techniques are tested. The most suitable one is selected to create seasonal maps for 2018 and 2021 with pollution concentrations for particulate matter values and nitrogen dioxide for each 1,000 m2 cell within the administrative boundaries. Based on the evaluated pollution particulate matter values, which are used as additional variables for semi-parametric regressions the impact of the air quality on rents is estimated.FindingsThe findings reveal a compelling association between air quality and the economic aspect of the residential real estate market, with noteworthy implications for both tenants and property investors. The relationship between air pollution variables and rents is statistically significant. However, there is only a “willingness-to- pay” for low particulate matter values, but not for nitrogen dioxide concentrations. With good air quality, residents in Berlin are willing to pay a higher rent (3%).Practical implicationsThese results suggest that a “marginal willingness-to-pay” occurs in a German city. The research underscores the multifaceted impact of air quality on the residential rental market in Berlin. The evidence supports the notion that a cleaner environment not only benefits human health and the planet but also contributes significantly to the economic bottom line of property investors.Originality/valueThe paper has a unique data engineering approach. It collects spatiotemporal data from network of state-certified measuring sites to create an index of air pollution. This spatial information is merged with residential listings. Afterward non-linear regression models are estimated.