Automated mass appraisal system with cross-city evaluation capability: a test development in China

The appraisal of property value is extremely important in a modern economy. For example, developers and end-consumers use appraisals for their investment decisions. Governments use it for taxation purposes, while banks rely on appraisals to update their risk profile when managing mortgage and credit application activities. With fast developing economies, quickly valuing new cities and suburbs as they get built becomes particularly difficult. Globalisation has also increased the need for common international valuation standards and automated methods. This research investigates the present mass appraisal systems and the role of automated valuation models. Financial institutions and institutional investors are increasingly more concerned about constantly updating their present portfolio value especially in a dynamic market. Trends of significant peaks and troughs need to be accounted in a faster cycle time with short bursts of pricing adjustments. The problem poses a challenge because property transactions are infrequently traded unlike other commodities such as securities. Hence, there are not many recent transactions for the same property to receive an updated value with a simple adjustment based on economic conditions. The study proposes a method that solves both large-scale mass appraisal with an ability to search across cities to discover properties with similar characteristics for its update and comparison scheme. This research advances the automated valuation model for the residential property market with a test development performed in China. In particular, the resulting model was tested with data from Chinese Tier 1, 2 and 3 Cities to evaluate property values. This research performs several major accomplishments. First, it demonstrates the efficacy of reducing human cognitive effort in the mass appraisal exercise. Second, by applying Artificial Neural Network capabilities in the automated valuation model, pricing of residential properties are able to draw upon knowledge from more mature cities with greater number of transactions and apply to newer developments in less developed cities. Third, the proposed mass appraisal system shows the reliability and robustness that matches the rapid development of Chinas real estate market that had been verified by a real application. Finally, the approach developed provides a valuable new method for property valuation that reduces the possible bias, increases consistency and lowers the effort required by current manual methods, with a lower data requirement

Urban Housing Patterns in a tide of change:

The development of the housing markets in different European metropolitan areas is of high interest for the urban development and the real estate markets, which are moving towards globalisation. The Budapest housing market is an ideal candidate for scrutiny from an institutional and evolutionary perspective due to its fragmented nature: different house types, age categories, price levels and micro-locations are found side by side. This is a case ‘in between’ Eastern and Western settings, with its own distinctive path dependence – its development pattern does not resemble any other system. The study comprises an innovative economic analysis of the Budapest housing market structure. Applying the self-organising map and the learning vector quantification sheds light on how physical and socio-demographic characteristics, price and regulation are related in this market. Further analysis is carried out using the analytical hierarchy process together with in-depth interviews of experts and a case study of urban renewal in two neighbourhoods using market data. The results are compared with those of a prior study from Helsinki and Amsterdam, as well as with more general theory literature. The results suggest a great difficulty in relating the empirical findings from Budapest to mainstream theory of housing markets

Gaining a competitive advantage through market research into home buyer preferences

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Architecture, 1992.Includes bibliographical references (leaves 126-129).by Arthur Paul Boytinck and Maaria Indermuehle Olander.M.S

From metaheuristics to learnheuristics: Applications to logistics, finance, and computing

Un gran nombre de processos de presa de decisions en sectors estratègics com el transport i la producció representen problemes NP-difícils. Sovint, aquests processos es caracteritzen per alts nivells d'incertesa i dinamisme. Les metaheurístiques són mètodes populars per a resoldre problemes d'optimització difícils en temps de càlcul raonables. No obstant això, sovint assumeixen que els inputs, les funcions objectiu, i les restriccions són deterministes i conegudes. Aquests constitueixen supòsits forts que obliguen a treballar amb problemes simplificats. Com a conseqüència, les solucions poden conduir a resultats pobres. Les simheurístiques integren la simulació a les metaheurístiques per resoldre problemes estocàstics d'una manera natural. Anàlogament, les learnheurístiques combinen l'estadística amb les metaheurístiques per fer front a problemes en entorns dinàmics, en què els inputs poden dependre de l'estructura de la solució. En aquest context, les principals contribucions d'aquesta tesi són: el disseny de les learnheurístiques, una classificació dels treballs que combinen l'estadística / l'aprenentatge automàtic i les metaheurístiques, i diverses aplicacions en transport, producció, finances i computació.Un gran número de procesos de toma de decisiones en sectores estratégicos como el transporte y la producción representan problemas NP-difíciles. Frecuentemente, estos problemas se caracterizan por altos niveles de incertidumbre y dinamismo. Las metaheurísticas son métodos populares para resolver problemas difíciles de optimización de manera rápida. Sin embargo, suelen asumir que los inputs, las funciones objetivo y las restricciones son deterministas y se conocen de antemano. Estas fuertes suposiciones conducen a trabajar con problemas simplificados. Como consecuencia, las soluciones obtenidas pueden tener un pobre rendimiento. Las simheurísticas integran simulación en metaheurísticas para resolver problemas estocásticos de una manera natural. De manera similar, las learnheurísticas combinan aprendizaje estadístico y metaheurísticas para abordar problemas en entornos dinámicos, donde los inputs pueden depender de la estructura de la solución. En este contexto, las principales aportaciones de esta tesis son: el diseño de las learnheurísticas, una clasificación de trabajos que combinan estadística / aprendizaje automático y metaheurísticas, y varias aplicaciones en transporte, producción, finanzas y computación.A large number of decision-making processes in strategic sectors such as transport and production involve NP-hard problems, which are frequently characterized by high levels of uncertainty and dynamism. Metaheuristics have become the predominant method for solving challenging optimization problems in reasonable computing times. However, they frequently assume that inputs, objective functions and constraints are deterministic and known in advance. These strong assumptions lead to work on oversimplified problems, and the solutions may demonstrate poor performance when implemented. Simheuristics, in turn, integrate simulation into metaheuristics as a way to naturally solve stochastic problems, and, in a similar fashion, learnheuristics combine statistical learning and metaheuristics to tackle problems in dynamic environments, where inputs may depend on the structure of the solution. The main contributions of this thesis include (i) a design for learnheuristics; (ii) a classification of works that hybridize statistical and machine learning and metaheuristics; and (iii) several applications for the fields of transport, production, finance and computing

Artificial Intelligence for autonomous persona generation to shape tailored communications and products and incentivise disaster preparation behaviours

Elizabeth Ditton investigated whether machine learning, specifically clustering algorithms, could be used to mimic expert decision making used for targeted disaster preparation messaging. She found that clustering algorithms could be used to develop personas that achieve the same level of depth and nuance as manually developed personas, without the required resources

The Formation of a Cross-Selling Initiative Climate and its Interplay with Service Climate

Purpose— This study explores the formation and consequences of a cross-selling initiative climate, as well as how a service climate, which provides an important boundary condition, affects both its formation and its ultimate impact on service–sales performance. This article identifies two important predictors of a cross-selling initiative climate: frontline employees’ perceptions of supervisors’ bottom-line mentality and their own sense of accountability. Design/methodology/approach— The multilevel data set includes 180 frontline staff and supervisors (team leaders) from 31 teams employed by a spa/beauty salon chain. Hierarchical linear modelling and partial least squares methods serve to analyse the data. Findings— Supervisors’ bottom-line mentality disrupts a cross-selling initiative climate; a sense of accountability exerts a positive impact, at both individual and team levels. A service climate at the team level weakens the impact of a sense of accountability on a cross-selling initiative climate. A cross-selling initiative climate has a positive effect on team-level service–sales performance, but this effect is weakened by the service climate. Originality/value— This study conceptualises an important frontline work unit attribute as a climate. It offers an initial argument that a cross-selling initiative climate is a central factor driving a work unit’s service–sales performance, which can increase firms’ productivity and competitive advantages. With this initial attempt to explore the antecedents and consequences of a cross-selling initiative climate, the study also offers novel insights into the interplay between a service and a cross-selling initiative climate

Causal impacts of transport interventions on air quality

The transport sector is one of the main sources of air pollution emissions, particularly for carbon monoxide, nitrogen oxides, and particulate matter. Evaluating the effectiveness of transport interventions on improving air quality is essential to informing future policy. However, a comparison of air quality observations before and after an intervention can be biased by various factors, such as weather conditions and seasonality effects. Causal inference methods generally have advantages in intervention evaluation in terms of data requirements, model building, and the interpretation of effect estimates. Causality goes beyond statistical association in the sense that it seeks to measure the net effect of an intervention on an outcome through all possible pathways directing from the intervention to the outcome. Causal inference methods have been applied to address the same question, however, the important confounders (such as weather conditions) are commonly controlled for by including variables in the causal inference model and assuming a parametric relationship. The thesis focuses on understanding the causal impacts of transport interventions on air quality. A novel ex-post policy evaluation framework, combining meteorological normalisation, change point detection, and causal inferencing, is proposed to overcome the limitations of previous approaches, and it is applied to three distinct transport interventions: improving public transport supply (Jubilee Line Extension), tightening road traffic emission standards (London Ultra Low Emission Zone), and restricting both transport activities and supply (COVID-19 lockdown). The Jubilee Line extension led to only small (< 1%) or insignificant changes in air pollution on average in London. The Ultra Low Emission Zone showed an average reduction of less than 3% for NO2 concentrations and insignificant effects on O3 and PM2.5 concentrations. The lockdown reduced the NO2 concentrations in London by less than 12% on average, and it had an insignificant effect on O3, PM10, and PM2.5. Therefore, the empirical results of the thesis consistently highlight the necessity of a multi-faceted set of policies that aim to reduce emissions across sectors with coordination among local, regional, and national government in order to achieve long-term improvements in air quality in cities.Open Acces

Consumer Data Research

Big Data collected by customer-facing organisations – such as smartphone logs, store loyalty card transactions, smart travel tickets, social media posts, or smart energy meter readings – account for most of the data collected about citizens today. As a result, they are transforming the practice of social science. Consumer Big Data are distinct from conventional social science data not only in their volume, variety and velocity, but also in terms of their provenance and fitness for ever more research purposes. The contributors to this book, all from the Consumer Data Research Centre, provide a first consolidated statement of the enormous potential of consumer data research in the academic, commercial and government sectors – and a timely appraisal of the ways in which consumer data challenge scientific orthodoxies