A model for predicting price polarity of real estate properties using information of real estate market websites

This paper presents a model that uses the information that sellers publish in real estate market websites to predict whether a property has higher or lower price than the average price of its similar properties. The model learns the correlation between price and information (text descriptions and features) of real estate properties through automatic identification of latent semantic content given by a machine learning model based on doc2vec and xgboost. The proposed model was evaluated with a data set of 57,516 publications of real estate properties collected from 2016 to 2018 of Bogot\'a city. Results show that the accuracy of a classifier that involves text descriptions is slightly higher than a classifier that only uses features of the real estate properties, as text descriptions tends to contain detailed information about the property

The Role of Information in Real Estate Markets

The overall research aim of this dissertation is to investigate three different informational aspects, namely information demand, information availability, and information supply, and their impact and predictive abilities with respect to both direct and indirect real estate markets. The three contributing papers of this dissertation reveal that newly emerging information sources and channels, combined with innovative analyzing tools, enable more accurate predictions of future market movements

Improving residential housing project purchase by using integrated multi-attribute decision making and sentiment analysis technique

The residential house purchase decision making is highly complex due to reasons such as conflicting criteria which is hard to model, infrequent type of decisions, uncertain and irreversible decision outcomes, high investment, and long-term financial burden. Unlike many other types of purchasing, housing purchase decision-making is riskier and sometimes even ‘traumatic’. It is often associated with feeling of regret and the possibility of loss among homebuyers. Typically, the Multi Attribute Decision Making (MADM) models are used to systematically assist and structure residential housing project selection decision making. However, the MADM models impose deficiencies in the evaluation process due to insufficient knowledge of homebuyers, ignorance of public opinions and limited sources of information. Furthermore, the application of MADM models requires homebuyer to rely on their evaluation experience which potentially led to an imprecise decision. Hence, this study developed an improved model by integrating MADM and three approaches of Sentiment Analysis to capture and rank criteria from public opinions through online reviews. Properties online forums and google reviews were selected to extract public opinions through online reviews. Three high-rise residential projects located in Malaysia were used as case projects for demonstrating the model development and validation of the proposed framework. Three Sentiment Analysis approach were considered; Lexicon, Machine Learning and hybrid. Based on the ranking established by the models, it shows that location, facility, and house attributes are the most important criteria in residential housing purchase decision making. In addition, classification using a hybrid MADM Sentiment Analysis approach outperforms the Lexicon approach with better accuracy. The developed model can assist homebuyer in making decision for the current practice. Moreover, it can be generalised to other related multi-criteria applications with the use of online public opinions as reference

Securing Coconut Availability in Indonesia

Coconut shortage has been a concerning issue in Indonesia for the past few years. Using a system dynamics (SD) approach and the supply and demand ratio (S/D ratio), this study aims to build a simulation model for the sustainability of coconut supply in Indonesia. The result shows that the S/D ratio will fall below 1 by 2023, indicating that the supply will no longer meet the demands. The model suggests that the most effective policy is doubling the coconut plantations areas and increasing the yield up to 2 tons/ha of copra

A treatise on Web 2.0 with a case study from the financial markets

There has been much hype in vocational and academic circles surrounding the emergence of web 2.0 or social media; however, relatively little work was dedicated to substantiating the actual concept of web 2.0. Many have dismissed it as not deserving of this new title, since the term web 2.0 assumes a certain interpretation of web history, including enough progress in certain direction to trigger a succession [i.e. web 1.0 → web 2.0]. Others provided arguments in support of this development, and there has been a considerable amount of enthusiasm in the literature. Much research has been busy evaluating current use of web 2.0, and analysis of the user generated content, but an objective and thorough assessment of what web 2.0 really stands for has been to a large extent overlooked. More recently the idea of collective intelligence facilitated via web 2.0, and its potential applications have raised interest with researchers, yet a more unified approach and work in the area of collective intelligence is needed. This thesis identifies and critically evaluates a wider context for the web 2.0 environment, and what caused it to emerge; providing a rich literature review on the topic, a review of existing taxonomies, a quantitative and qualitative evaluation of the concept itself, an investigation of the collective intelligence potential that emerges from application usage. Finally, a framework for harnessing collective intelligence in a more systematic manner is proposed. In addition to the presented results, novel methodologies are also introduced throughout this work. In order to provide interesting insight but also to illustrate analysis, a case study of the recent financial crisis is considered. Some interesting results relating to the crisis are revealed within user generated content data, and relevant issues are discussed where appropriate

4th. International Conference on Advanced Research Methods and Analytics (CARMA 2022)

Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information. As these sources, methods, and applications become more interdisciplinary, the 4th International Conference on Advanced Research Methods and Analytics (CARMA) is a forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges. Due to the covid pandemic, CARMA 2022 is planned as a virtual and face-to-face conference, simultaneouslyDoménech I De Soria, J.; Vicente Cuervo, MR. (2022). 4th. International Conference on Advanced Research Methods and Analytics (CARMA 2022). Editorial Universitat Politècnica de València. https://doi.org/10.4995/CARMA2022.2022.1595

5th International Conference on Advanced Research Methods and Analytics (CARMA 2023)

Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information. As these sources, methods, and applications become more interdisciplinary, the 5th International Conference on Advanced Research Methods and Analytics (CARMA) is a forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges.Martínez Torres, MDR.; Toral Marín, S. (2023). 5th International Conference on Advanced Research Methods and Analytics (CARMA 2023). Editorial Universitat Politècnica de València. https://doi.org/10.4995/CARMA2023.2023.1700

Essays on sentiment: an analysis of the commercial real estate market

This thesis deals with the extraction, construction and analysis of commercial real estate (CRE) sentiment within Europe and the U.K. especially. The three empirical studies in this thesis may contribute to our understanding of the discipline. As I establish in the literature review, the analysis of commercial real estate sentiment still offers a lot of potential for further research. Since real estate markets are subject to sentiment swings, scholars and market participants should consider them in their market analysis. The first study establishes the need for sentiment consideration within the European real estate market. In order to justify the research of sentiment analysis, I have used different indirect and direct sentiment proxies and applied them in yield models for 80 different commercial property (sub-)markets within Europe. The statistical modification of different sentiment proxies is needed since not all European property markets offer direct sentiment measures. The results suggest, that the consideration of sentiment in a yield model framework adds significant information. I found, that CRE markets, which are assumed to be more liquid and developed, show a larger exposure to property specific sentiment measures. Markets, which are assumed to be less developed (i.e. Eastern European markets) on the other hand, have a larger exposure to more general macroeconomic sentiment indicators. The second study introduces a new method, which can be used to extract sentiment from text documents. The primary motivation for the use of text documents and the application of Natural Language Processing (NLP) methods lies in the fact that these documents are published much faster than other sentiment proxies. This allows extracting a much more accurate market sentiment. The second study should be understood as an introductory chapter to the method and the field of NLP. In total four different wordlists (AFINN, BING, NRC and TM) are used to extract the sentiment form various market reports for the CRE market in U.K. The study reveals that sentiment extracted from those documents, can be used to improve autocorrelated models. The last study uses those findings and applies different supervised learning methods. While the second study has produced sufficient results, the underlying text corpus of market reports has shown a series of insufficiencies. I have therefore, used a large dataset of more than 120,000 news articles, all concerning the British CRE market. Findings suggest, that the main issue of supervised learning algorithms is the appropriate classification of the different entities. I offer two approaches in order to construct robust sentiment indicators

Essays on the New Blockchain-Based Digital Financial Market : Risks and Opportunities

This doctoral thesis consists of five original essays on the risks and opportunities of the new blockchain-based digital financial market. The purpose of this dissertation is to analyze, identify, and, if possible, predict some of the major risks in the market for blockchain-based digital assets. It analyzes how crypto-specific characteristics are associated with solvency risk, sustainability risk, seclusion risk, and sentiment risk. On top of that, it also sheds light on the opportunity side of this financial innovation. The first essay of this dissertation specifically focuses on cryptocurrency for solvency risks. To forecast potential cryptocurrency default at an early stage, this study focuses on variables that are part of the information set of the investor 1 month at most after the start of trading for a cryptocurrency. The results of this research show that bankruptcies among cryptocurrencies are predictable. The second essay explores energy risk as a fundamental market-driving force for the pricing of cryptocurrency. Cryptocurrencies using a high-energy-consumption consensus protocol are riskier than others because their mining costs are more exposed to changes in energy price. Surprisingly, the study finds that energy consumption does not seem to play a role in pricing cryptocurrency. The third essay hypothesizes that privacy coins form a distinct submarket in the cryptocurrency market, shedding light on seclusion risk. It shows that privacy coins and non-privacy coins are two distinct asset markets within the cryptocurrency market. The fourth essay is about news media sentiment risk. It explores whether news media sentiments have an impact on Bitcoin volatility. It also differentiates financial sentiment and psychological sentiment and finds that financially optimistic investors are driving the Bitcoin market. On the other hand, the fifth essay in this dissertation analyzes opportunities, especially the funding opportunity in the widely known category of new digital assets defined as crypto tokens. It analyzes the determinants of the success of initial coin offerings and finds that initial-coin-offering investors are largely guided by their emotions when making investment decisions. Surprisingly, regulatory framework has not yet become a priority among policymakers. Therefore, this doctoral dissertation not only facilitates future research, but also helps regulators in shaping the future of blockchain-based financial technologies.Tämä väitöskirja koostuu viidestä esseestä, jotka käsittelevät uuden lohkoketjupohjaisen digitaalisen rahoitusmarkkinan riskejä ja mahdollisuuksia. Väitöskirjan tarkoituksena on analysoida, tunnistaa ja mahdollisuuksien mukaan ennustaa joitakin lohkoketjupohjaisten digitaalisten varojen markkinoiden suurimpia riskejä. Siinä analysoidaan, miten kryptovaluuttakohtaiset ominaisuudet liittyvät vakavaraisuusriskiin, kestävyysriskiin, eristäytymisriskiin ja sentimenttiriskiin. Tämän lisäksi se valottaa myös tämän rahoitusinnovaation mahdollisuuksia. Tämän väitöskirjan ensimmäisessä esseessä keskitytään erityisesti kryptovaluuttaan maksukyvyttömyysriskinä. Tässä tutkimuksessa keskitytään muuttujiin, jotka ovat sijoittajan saatavilla korkeintaan 1 kuukausi sen jälkeen, kun kaupankäynti kryptovaluutalla on alkanut. Tämän tutkimuksen tulokset osoittavat, että kryptovaluuttojen konkurssit ovat ennustettavissa. Toisessa esseessä tutkitaan energiariskiä markkinoita ohjaavana voimana kryptovaluutan hinnoittelussa. Kryptovaluutat, jotka käyttävät paljon energiaa kuluttavaa konsensusprotokollaa, ovat muita riskialttiimpia, koska niiden louhintakustannukset ovat alttiimpia energian hinnan muutoksille. Yllättäen tutkimuksessa todetaan, että energiankulutuksella ei näytä olevan merkitystä kryptovaluuttojen hinnoittelussa. Kolmannessa esseessä hypoteesina on, että yksityisyyskolikot muodostavat erillisen alamarkkinan kryptovaluuttamarkkinoilla, ja tutkimus tarkastelee näiden eristäytymisriskiä. Siinä osoitetaan, että yksityisyyskolikot ja ei-yksityisyyskolikot ovat kaksi erillistä omaisuuserämarkkinaa kryptovaluuttamarkkinoilla. Neljäs essee käsittelee uutismedian sentimenttiriskiä. Siinä tutkitaan, vaikuttaako uutismedian sentimentti Bitcoinin volatiliteettiin. Siinä myös erotetaan toisistaan taloudellinen sentimentti ja psykologinen sentimentti ja todetaan, että taloudellisesti optimistiset sijoittajat ohjaavat Bitcoin-markkinoita. Väitöskirjan viidennessä esseessä analysoidaan mahdollisuuksia, erityisesti rahoitusmahdollisuuksi, liittyen laajalti tunnettuihin digitaalisiin tokeneihin. Siinä havaitaan, että näihin omaisuuseriin sijoittavat sijoittajat toimivat pitkälti tunteidensa ohjaamina sijoituspäätöksiä tehdessään. Yllättävää kyllä, sääntelykehyksestä ei ole vielä tullut poliittisten päättäjien prioriteettia. Siksi tämä väitöskirja ei ainoastaan tue tulevaa tutkimusta, vaan auttaa myös viranomaisia lohkoketjupohjaisten rahoitusteknologioiden tulevaisuuden määrittelyssä.fi=vertaisarvioitu|en=peerReviewed