{
  "format": "dataset-manifest/1",
  "slug": "real-estate-transactions-database",
  "version": 2,
  "seed": "real-estate-transactions-database:1",
  "spec": {
    "format": "dataset/1",
    "slug": "real-estate-transactions-database",
    "name": "Real Estate Transactions Insights",
    "tagline": "Real estate transactions including property features, client demographics, and financial aspects of sales.",
    "description": "Agents navigate the real estate market, guiding clients through the buying and selling of properties. Each transaction represents a unique interaction between a buyer and a seller, often influenced by property features such as size, location, and condition. The transactions encapsulate the financial undertakings, including details about mortgages and deposits, while also reflecting the demographics of clients involved in these exchanges. Popular neighborhoods tend to see higher volumes of transactions, where competitive offers and standard inspections are the norms, contrasting with less common properties that may offer distinctive features or utilize rare escrow companies.\n\nThe primary table in this dataset, 'transactions', holds vital information on each property exchange. A single row in the transactions table captures a complete transaction record, detailing property characteristics, client demographics, inspection outcomes, and financial commitments. This structure enables various analytical opportunities, allowing data practitioners to explore relationships between property attributes and transaction success, as well as trends in client demographics across different market segments.\n\nThis dataset provides the opportunity to answer pertinent questions such as: What are the key factors influencing property transaction prices? How do client demographics correlate with property types or locations? Are there trends in inspection results that could affect buyer confidence? The synthetic nature of this data ensures that it remains a safe and versatile resource for practicing data analysis, enabling developers to hone their skills in handling real estate transactions and gaining insights into this dynamic industry.",
    "domain": "real-estate",
    "shape": "transactional",
    "useCases": [
      "sql-practice",
      "orm-seeding"
    ],
    "keywords": [
      "real estate",
      "transactions",
      "properties",
      "clients",
      "mortgages",
      "inspections",
      "neighborhoods",
      "data analysis"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "transactions": {
        "description": "Each row represents a unique real estate transaction, linking various property features, client details, and financial aspects. The table provides a comprehensive overview of the real estate environment, with columns detailing property types, client demographics, and transaction specifics.",
        "rows": 500,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each transaction."
          },
          "property_type": {
            "kind": "enum",
            "values": [
              "single_family",
              "condo",
              "townhouse",
              "multi_family",
              "land"
            ],
            "description": "Type of property involved in the transaction.",
            "weights": [
              6,
              2,
              1,
              1,
              1
            ]
          },
          "square_footage": {
            "kind": "int",
            "min": 500,
            "max": 5000,
            "description": "Total area of the property in square feet."
          },
          "neighborhood": {
            "kind": "enum",
            "values": [
              "Downtown",
              "Suburbs",
              "Countryside",
              "City Center",
              "Coastal"
            ],
            "description": "Neighborhood where the property is located."
          },
          "client_age": {
            "kind": "int",
            "min": 18,
            "max": 80,
            "description": "Age of the client involved in the transaction.",
            "distribution": "normal"
          },
          "client_income": {
            "kind": "decimal",
            "min": 25000,
            "max": 300000,
            "scale": 2,
            "description": "Annual income of the client in USD.",
            "distribution": "long-tail"
          },
          "mortgage_amount": {
            "kind": "decimal",
            "min": 5000,
            "max": 1000000,
            "scale": 2,
            "description": "Amount of mortgage taken for the property.",
            "distribution": "heavy-tail"
          },
          "deposit_amount": {
            "kind": "decimal",
            "min": 500,
            "max": 50000,
            "scale": 2,
            "description": "Deposit amount made during the transaction.",
            "distribution": "long-tail"
          },
          "inspection_result": {
            "kind": "enum",
            "values": [
              "pass",
              "fail"
            ],
            "description": "Result of the property inspection.",
            "weights": [
              9,
              1
            ]
          },
          "transaction_date": {
            "kind": "date",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "Date when the transaction occurred.",
            "trend": "growing"
          }
        }
      }
    },
    "structure": "single-table",
    "traits": [
      "categorical",
      "monetary",
      "nullable"
    ],
    "analysisQuestions": [
      "What are the most common types of properties being sold?",
      "How does client income vary across different neighborhoods?",
      "What percentage of transactions pass the inspection?",
      "What is the average mortgage amount for single-family homes?",
      "How do deposits differ based on property type?"
    ],
    "suggestedQueries": [
      {
        "title": "List all transactions in a specific neighborhood",
        "sql": "SELECT * FROM transactions WHERE neighborhood = 'Downtown';",
        "dialect": "postgres"
      },
      {
        "title": "Count transactions by property type",
        "sql": "SELECT property_type, COUNT(*) FROM transactions GROUP BY property_type;",
        "dialect": "postgres"
      },
      {
        "title": "Average client income by property type",
        "sql": "SELECT property_type, AVG(client_income) FROM transactions GROUP BY property_type;",
        "dialect": "postgres"
      },
      {
        "title": "Find inspection pass rates",
        "sql": "SELECT inspection_result, COUNT(*) * 100.0 / (SELECT COUNT(*) FROM transactions) AS pass_rate FROM transactions GROUP BY inspection_result;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Real Estate Agent Analysis",
        "body": "Real estate agents can utilize this dataset to analyze trends in property sales, helping them to identify which types of properties are most popular among clients. By examining factors such as neighborhood preferences and client demographics, agents can tailor their sales strategies to better meet the needs of potential buyers."
      },
      {
        "title": "Market Research for Investors",
        "body": "Investors looking to gain insights into the real estate market can leverage this dataset to assess which areas are seeing the most activity and what types of properties are yielding the highest returns. By understanding client income levels and mortgage trends, they can make informed decisions about where to invest their resources."
      },
      {
        "title": "Academic Research",
        "body": "Researchers studying the effects of economic factors on real estate transactions can use this dataset to facilitate their analyses. The data allows for exploration of relationships between client income, property types, and inspection outcomes, contributing valuable insights to the field of real estate economics."
      }
    ]
  },
  "counts": {
    "transactions": 500
  },
  "stats": {
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        "deposit_amount": {
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        "inspection_result": {
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          "min": "2020-01-05",
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    }
  },
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