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Property Management Data Analytics

Previously, real estate investments were based on speculation, informed estimates, and assumptions, which frequently repeated the same mistakes. Real estate data science provides insight to help people make better-informed decisions about their investments, thereby significantly reducing their risk. The result is that investors are increasingly leveraging data to strengthen their competitive advantage.<br><br>

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Property Management Data Analytics

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  1. Property Management Data Analytics 01 DESCRIPTIVE ANALYTICS In descriptive analytics, historical data is analyzed to create valuable historical insights. For example, what is the yearly growth rent in real estate over the next five years? 02 The primary purpose of this type of analysis is to establish what activities need implementing to take advantage of an opportunity or to avoid a future problem, such as determining the recommended number of beds for an Airbnb house. PRESCRIPTIVE ANALYTICS 03 DIAGNOSTIC ANALYTICS This involves analyzing historical data to determine what caused a specific event. Diagnosing rental properties, for example, can reveal why they have a very high turnover rate. 04 These analytics predict future events, as its name implies. For example, diagnostic and descriptive analytics enables users to detect clusters, exceptions, and trends in the housing market, such as how much rent will rise over the next two years. PREDICTIVE DATA ANALYTICS 05 WHAT IS BIG DATA Big data in commercial real estate includes everything from leasing, financial, and demographic data to building system information and tenant movements inside buildings. Big data also include monitoring social media and news feeds.

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