Paper: arXiv 2403.00273

Abstract

The Argentinian real estate market presents a unique case study characterized by its unstable and rapidly shifting macroeconomic circumstances over the past decades. Despite the existence of a few datasets for price prediction, there is a lack of mixed modality datasets specifically focused on Argentina. In this paper, the first edition of ARED is introduced. A comprehensive real estate price prediction dataset series, designed for the Argentinian market. This edition contains information solely for Jan-Feb 2024. It was found that despite the short time range captured by this zeroth edition (44 days), time dependent phenomena has been occurring mostly on a market level (market as a whole). Nevertheless future editions of this dataset, will most likely contain historical data. Each listing in ARED comprises descriptive features, and variable-length sets of images.

Complexity vs Empirical Score

  • Math Complexity: 0.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper is a dataset publication with no mathematical models or derivations, but it includes detailed data scraping methodology, visualization of statistical metrics (Wasserstein distance, quantile ranges), and a plan for future updates, making it highly implementation-heavy and ready for backtesting.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Argentine Real Estate Dataset Creation"] --> B["Methodology<br>Data Collection Jan-Feb 2024"]
  B --> C["Input Data<br>Property Listings + Images"]
  C --> D["Computational Process<br>Mixed Modality Data Aggregation"]
  D --> E["Key Finding 1<br>Market-Level Time-Dependent Phenomena"]
  D --> F["Key Finding 2<br>Lack of Historical Data"]
  E --> G["Outcome<br>ARED: First Edition Dataset"]
  F --> G

Cited References (8)

  1. El origen de la dolarizaci 'o n inmobiliaria en la Argentina (2013)
  2. Property Listings for 5 South American Countries (2020)
  3. ST-RAP: A Spatio-Temporal Framework for Real Estate Appraisal (2023)
  4. Vision-based Real Estate Price Estimation (2017) — link
  5. Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting (2021)
  6. A Contextual Master-Slave Framework on Urban Region Graph for Urban Village Detection (2023) — link
  7. Spatial-temporal transformer networks for traffic flow forecasting (2020) — link
  8. Informer: Beyond efficient transformer for long sequence time-series forecasting (2021)

0 of 8 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.