Abstract
The effect of space on real estate prices is critical due to the inherent nature of real estate properties. There exist numerous methodologies in order to incorporate the effect of space into the analysis including the addition of district variables or the use spatial econometric models. Spatial models help with the identification and inference problems compared to the simple hedonic models, yet are harder to work with due to their complexity and resource requirements. In this study, using geospatial data, the authors explain the spatial econometric models and tools available for the analysis of cross sectional real estate data.
TopBackground
In this section, the spatial econometric models for the cross-sectional data are provided. The models are explained first and then a discussion is provided. Next section explains the spatial autocorrelation measures.
Key Terms in this Chapter
R: Free programming language for statistical computing and graphics in order to clean, analyze and graph data. It is has many advantages over alternatives including platform independency, many available packages available and compatibility with machine learning.
Geolocation: The identification of location of an item using sources such as GPS and internet. It enables obtaining real time information and locating the item with high accuracy at any given point in time.
Moran’s I: A widely used measure for spatial autocorrelation by P.A.P. Moran. It measures how similar one object is to others surrounding it.
Big Data: Data that is very large to be analyzed using traditional models and tools. Different forms exists including structured data such as address or age and unstructured data such as e-mails or records. The biggest sources of big data are social media networks and platforms.
Open Source: The term refers to being freely accessible by public in order to modify and share. It provides transparency, flexibility and cost efficiency. Open source licensing benefits innovation through collaboration.
Cloud Computing: On demand access to computing resources using the internet. The resources include data storage, servers, databases, networking and software. Cost saving, speed, performance and security are among the benefits of using cloud computing services.
Spatial: Relating to space. Spatial data, also referred to as geographic data or geospatial data, provides information to identify location. There are basically two different types of spatial data; feature data such as a road or a building, and coverage data such as a satellite image or an aerial photograph.