Identifying Real Estate Opportunities using Machine Learning

The real estate market is exposed to many fluctuations in prices because of existing correlations with many variables, some of which cannot be controlled or might even be unknown. Housing prices can increase rapidly (or in some cases, also drop very fast), yet the numerous listings available online where houses are sold or rented are not likely to be updated that often. In some cases, individuals interested in selling a house (or apartment) might include it in some online listing, and forget about updating the price. In other cases, some individuals might be interested in deliberately setting a price below the market price in order to sell the home faster, for various reasons. In this paper, we aim at developing a machine learning application that identifies opportunities in the real estate market in real time, i.e., houses that are listed with a price substantially below the market price. This program can be useful for investors interested in the housing market. We have focused in a use case considering real estate assets located in the Salamanca district in Madrid (Spain) and listed in the most relevant Spanish online site for home sales and rentals. The application is formally implemented as a regression problem that tries to estimate the market price of a house given features retrieved from public online listings. For building this application, we have performed a feature engineering stage in order to discover relevant features that allows for attaining a high predictive performance. Several machine learning algorithms have been tested, including regression trees, k-nearest neighbors, support vector machines and neural networks, identifying advantages and handicaps of each of them.Comment: 24 pages, 13 figures, 5 table

Diseño de una aplicación de monitoreo de emanación de gases en la ciudad mediante el uso de tecnologías de inteligencia artificial

El presente trabajo de investigación realiza una revisión de referencias bibliográficas relacionadas a sistemas de monitoreo y modelos de pronóstico de contaminación atmosférica basados en técnicas de inteligencia artificial. La revisión consta principalmente de artículos de investigación científica obtenidos de bases de datos revistas indexadas, donde se exponen sus principales hallazgos, así como se presentan puntos de encuentro y desencuentro entre los diferentes autores. Finalmente, se presentan las conclusiones obtenidas a partir de la síntesis de la información revisada.Trabajo de investigaciónCampus Lima Centr

Regression Models to Predict Air Pollution from Affordable Data Collections

Air quality monitoring is key in assuring public health. However, the necessary equipment to accurately measure the criteria pollutants is expensive. Since the countries with more serious problems of air pollution are the less wealthy, this study proposes an affordable method based on machine learning to estimate the concentration of PM2.5. The capital city of Ecuador is used as case study. Several regression models are built from features of different levels of affordability. The first result shows that cheap data collection based on web traffic monitoring enables us to create a model that fairly correlates traffic density with air pollution. Building multiple models according to the hourly occurrence of the pollution peaks seems to increase the accuracy of the estimation, especially in the morning hours. The second result shows that adding meteorological factors allows for a significant improvement of the prediction of PM2.5 concentrations. Nevertheless, the last finding demonstrates that the best predictive model should be based on a hybrid source of data that includes trace gases. Since the sensors to monitor such gases are costly, the last part of the chapter gives some recommendations to get an accurate prediction from models that consider no more than two trace gases

Modeling PM 2.5

Outdoor air pollution costs millions of premature deaths annually, mostly due to anthropogenic fine particulate matter (or PM2.5). Quito, the capital city of Ecuador, is no exception in exceeding the healthy levels of pollution. In addition to the impact of urbanization, motorization, and rapid population growth, particulate pollution is modulated by meteorological factors and geophysical characteristics, which complicate the implementation of the most advanced models of weather forecast. Thus, this paper proposes a machine learning approach based on six years of meteorological and pollution data analyses to predict the concentrations of PM2.5 from wind (speed and direction) and precipitation levels. The results of the classification model show a high reliability in the classification of low (25 µg/m3) and low (<10 µg/m3) versus moderate (10–25 µg/m3) concentrations of PM2.5. A regression analysis suggests a better prediction of PM2.5 when the climatic conditions are getting more extreme (strong winds or high levels of precipitation). The high correlation between estimated and real data for a time series analysis during the wet season confirms this finding. The study demonstrates that the use of statistical models based on machine learning is relevant to predict PM2.5 concentrations from meteorological data

Contaminación del Aire y Justicia Ambiental en Quito, Ecuador

We studied the state of air pollution in Quito, the trajectories to improve its quality, and the relation between pollutants and socioeconomic condition measured from the value of urban land. We focused on three pollutants: fine particulate matter, coarse particulate matter and sedimentable particles. We used secondary sources and databases of the Municipality of Quito. There have been policies and actions to improve air quality, focused on diverse sectors. Despite this, the three pollutants exceed what is recommended in national and international air quality standards. Moreover, there is an inverse relation between pollution and the value of urban land: those populations living in places with lower land rent value receive greater contamination. Although the regulations to improve air quality have had some achievements, in most cases they have not been accomplished.Investigamos el estado de la contaminación del aire en la ciudad de Quito, las trayectorias para mejorar su calidad, y la relación entre contaminantes y condición socioeconómica medida a partir del valor del suelo urbano. Nos concentramos en tres contaminantes: material particulado fino, material particulado grueso y partículas sedimentables. Utilizamos fuentes secundarias y bases de datos del Municipio de Quito. Han existido políticas y acciones para mejorar la calidad del aire, enfocadas en varios sectores. Pese a ello, los tres contaminantes estudiados sobrepasan lo recomendado en normas nacionales e internacionales de calidad del aire. Existe, además, una relación inversa entre contaminación y valor del suelo: las poblaciones que viven en lugares con menor valor del suelo reciben mayor contaminación. Las regulaciones para mejorar la calidad del aire, si bien han tenido algunos logros, en la mayoría de casos han sido incumplidas

Modeling PM2.5 Urban Pollution Using Machine Learning and Selected Meteorological Parameters

Outdoor air pollution costs millions of premature deaths annually, mostly due to anthropogenic fine particulate matter (or PM2.5). Quito, the capital city of Ecuador, is no exception in exceeding the healthy levels of pollution. In addition to the impact of urbanization, motorization, and rapid population growth, particulate pollution is modulated by meteorological factors and geophysical characteristics, which complicate the implementation of the most advanced models of weather forecast. Thus, this paper proposes a machine learning approach based on six years of meteorological and pollution data analyses to predict the concentrations of PM2.5 from wind (speed and direction) and precipitation levels. The results of the classification model show a high reliability in the classification of low (25 µg/m3) and low (<10 µg/m3) versus moderate (10–25 µg/m3) concentrations of PM2.5. A regression analysis suggests a better prediction of PM2.5 when the climatic conditions are getting more extreme (strong winds or high levels of precipitation). The high correlation between estimated and real data for a time series analysis during the wet season confirms this finding. The study demonstrates that the use of statistical models based on machine learning is relevant to predict PM2.5 concentrations from meteorological data