{
  "format": "dataset-manifest/1",
  "slug": "real-estate-transaction-insights",
  "version": 2,
  "seed": "real-estate-transaction-insights:1",
  "spec": {
    "format": "dataset/1",
    "slug": "real-estate-transaction-insights",
    "name": "Real Estate Transaction Insights",
    "tagline": "Property sales data reflecting housing market dynamics, including pricing fluctuations and neighborhood amenities.",
    "description": "Agents list properties for sale, showcasing their features and prices to attract potential buyers in a competitive market. Each property sale captures vital information, including the sale price, square footage, number of bedrooms and bathrooms, and neighborhood details. This data not only represents individual transactions but also reflects broader market trends, helping stakeholders understand how properties are valued in different locales.\n\nThe primary table, property_sales, contains rows that correspond to unique transactions of real estate properties. Each row includes details about the property sold like its address, characteristics, sale price, and the date of the transaction. By analyzing multiple entries, users can study pricing trends over time, identify which types of properties are selling faster than others, and assess the impact of nearby amenities on property values.\n\nThis dataset is particularly useful for developers and analysts looking to practice data manipulation and analysis in real estate contexts. It can help answer pressing questions such as: What factors most influence property prices in various neighborhoods? How do seasonal trends impact sales volume? What is the average time a property spends on the market before being sold? Through these inquiries, users can develop their skills in data visualization, statistical analysis, and market prediction.\n\nIt’s important to note that all data is synthetic, generated to mimic real-world scenarios while ensuring no personal information is included, making it a safe and ideal option for practice.",
    "domain": "real-estate",
    "shape": "time-series",
    "useCases": [
      "analytics-demo",
      "bi-dashboards"
    ],
    "keywords": [
      "real estate",
      "property sales",
      "market analysis",
      "housing trends",
      "transaction data"
    ],
    "locale": "en",
    "license": "CC0-1.0",
    "version": 1,
    "tables": {
      "property_sales": {
        "description": "Each row represents a property sale transaction, including details such as property type, sale price, and transaction date. The data links to various attributes that define the property and its location.",
        "rows": 1000,
        "columns": {
          "id": {
            "kind": "pk",
            "description": "Unique identifier for each property sale transaction."
          },
          "property_type": {
            "kind": "enum",
            "values": [
              "Residential",
              "Commercial",
              "Land"
            ],
            "description": "Type of property sold.",
            "weights": [
              7,
              2,
              1
            ]
          },
          "sale_price": {
            "kind": "decimal",
            "min": 50000,
            "max": 5000000,
            "scale": 2,
            "description": "Final sale price of the property.",
            "distribution": "long-tail"
          },
          "transaction_date": {
            "kind": "date",
            "from": "2020-01-01",
            "to": "2023-12-31",
            "description": "Date when the property was sold.",
            "trend": "growing"
          },
          "location": {
            "kind": "words",
            "min": 1,
            "max": 3,
            "description": "Description of the property's location.",
            "pool": [
              "downtown",
              "suburb",
              "rural",
              "coastal",
              "mountain",
              "urban",
              "historic",
              "modern",
              "scenic",
              "quiet",
              "busy",
              "accessible",
              "exclusive",
              "affordable",
              "luxury",
              "vibrant",
              "peaceful",
              "central",
              "remote",
              "developed",
              "emerging"
            ]
          },
          "amenities": {
            "kind": "words",
            "min": 1,
            "max": 3,
            "description": "List of key amenities available in the property.",
            "pool": [
              "pool",
              "gym",
              "garden",
              "parking",
              "balcony",
              "fireplace",
              "kitchen",
              "wifi",
              "security",
              "elevator",
              "laundry",
              "air_conditioning",
              "heating",
              "storage",
              "playground",
              "patio",
              "furnished",
              "cable",
              "smart_home",
              "landscaping"
            ]
          },
          "neighborhood": {
            "kind": "words",
            "min": 1,
            "max": 3,
            "description": "Name of the neighborhood where the property is located.",
            "pool": [
              "downtown",
              "uptown",
              "midtown",
              "waterfront",
              "historic",
              "suburban",
              "industrial",
              "commercial",
              "residential",
              "artistic",
              "family_friendly",
              "trendy",
              "quiet",
              "affluent",
              "diverse",
              "gated",
              "rural",
              "urban",
              "developing",
              "vibrant",
              "exclusive"
            ]
          },
          "school_proximity": {
            "kind": "int",
            "min": 0,
            "max": 5,
            "description": "Proximity to the nearest school (in miles)."
          },
          "transit_proximity": {
            "kind": "int",
            "min": 0,
            "max": 5,
            "description": "Proximity to public transit options (in miles)."
          }
        }
      }
    },
    "structure": "single-table",
    "traits": [
      "monetary",
      "timestamped",
      "categorical"
    ],
    "analysisQuestions": [
      "What are the average sale prices for different property types over time?",
      "How does the proximity to schools affect property prices?",
      "What trends can be identified in property sales across various neighborhoods?",
      "Are there seasonal patterns in real estate transactions?",
      "How do amenities influence the sale price of properties?"
    ],
    "suggestedQueries": [
      {
        "title": "Average Sale Price by Property Type",
        "sql": "SELECT property_type, AVG(sale_price) AS average_price FROM property_sales GROUP BY property_type;",
        "dialect": "postgres"
      },
      {
        "title": "Sales Over Time",
        "sql": "SELECT DATE_TRUNC('month', transaction_date) AS month, COUNT(*) AS sales_count FROM property_sales GROUP BY month ORDER BY month;",
        "dialect": "postgres"
      },
      {
        "title": "Top Neighborhoods by Sale Price",
        "sql": "SELECT neighborhood, AVG(sale_price) AS average_price FROM property_sales GROUP BY neighborhood ORDER BY average_price DESC LIMIT 10;",
        "dialect": "postgres"
      },
      {
        "title": "Sales by Proximity to Schools",
        "sql": "SELECT school_proximity, COUNT(*) AS sales_count FROM property_sales GROUP BY school_proximity;",
        "dialect": "postgres"
      }
    ],
    "usageScenarios": [
      {
        "title": "Market Trend Analysis",
        "body": "Real estate analysts can use this dataset to identify and analyze trends in the housing market over time. By examining different property types and their associated sale prices, they can offer insights into market conditions and forecast future trends."
      },
      {
        "title": "Investment Opportunity Identification",
        "body": "Investors looking to purchase properties can utilize this data to evaluate potential investment opportunities. By analyzing sales data in various neighborhoods, they can identify areas with high appreciation potential and make informed decisions."
      },
      {
        "title": "Property Valuation Studies",
        "body": "Real estate appraisers can leverage this dataset to understand pricing dynamics for different types of properties. By comparing sales data with property characteristics, they can provide accurate appraisals and valuations for clients."
      }
    ]
  },
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    "property_sales": 1000
  },
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    }
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