🏠 House Market Analytics – Power BI Report

This Power BI report delivers deep insights into the Danish housing market, analyzing sales trends, pricing metrics, property types, and regional dynamics using ~100,000 rows of housing data from Google BigQuery.

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📁 Dataset Overview

• Source: Google BigQuery (CSV format)
• Volume: ~100,000 rows × 19 columns (Columns A–S)
• Key Features: date, region, purchase_price, offer_price, sqm, sqm_price, sales_type, house_type, age

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📊 Page 1 – House Market Overview

🔍 Visuals

• Median Sales Change by Region
• Houses Sold in Latest Year & Quarter
• Sales in Last 12 Months
• Offer vs Purchase Price
• YOY Sales Growth by Sales Type

🧮 DAX Measures

Mediun Sales Change = 
VAR CurrMedianPrice = 
    MEDIANX(
        FILTER(Housing, YEAR(Housing[date]) = YEAR(MAX(Housing[date]))),
        Housing[purchase_price]
    )
VAR PrevMedianPrice = 
    MEDIANX(
        FILTER(Housing, YEAR(Housing[date]) = YEAR(MAX(Housing[date])) - 1),
        Housing[purchase_price]
    )
RETURN IF(PrevMedianPrice <> 0, (CurrMedianPrice - PrevMedianPrice) / PrevMedianPrice, BLANK())

Houses Sold on latest year and quarter = 
    CALCULATE(
        DISTINCTCOUNT(Housing[house_id]),
        YEAR(Housing[date]) = YEAR(MAX(Housing[date])) &&
        QUARTER(Housing[date]) = QUARTER(MAX(Housing[date]))
    )

Last 12 months Sales = 
    CALCULATE(
        SUM(Housing[purchase_price]),
        DATESINPERIOD(Housing[date], MAX(Housing[date]), -12, MONTH)
    )

YOY_SALES_GROWTH = 
VAR CurrYearSales = 
    CALCULATE(SUM(Housing[purchase_price]), YEAR(Housing[date]) = YEAR(MAX(Housing[date])))
VAR PrevYearSales = 
    CALCULATE(SUM(Housing[purchase_price]), YEAR(Housing[date]) = YEAR(MAX(Housing[date])) - 1)
RETURN IF(PrevYearSales <> 0, (CurrYearSales - PrevYearSales) / PrevYearSales, BLANK())

🧠 Insights

• YOY Median Price changes help detect pricing trends and inflation effects.
• Quarter-wise sales volumes show seasonal demand patterns.
• A clear view of last 12-month performance for rolling trend analysis.
• Offer vs Purchase gap suggests negotiation leverage.
• YOY Sales Growth highlights shifts in buying behavior by sales type.

📸 Screenshot – House Market Overview

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📊 Page 2 – Sales Performance

🔍 Visuals

• Sales by Region
• Key Influencers (AI Visual)
• Year-to-Date Sales Table
• Offer-to-SQM Ratio by Sales Type
• Average SQM Price by Region

🧮 DAX Measures

Sales by region = 
    CALCULATE(
        SUM(Housing[purchase_price]),
        ALLEXCEPT(Housing, Housing[region])
    )

Offer to SQM Ratio = 
    DIVIDE(SUM(Housing[Offer Price]), SUM(Housing[sqm]))

Total YTD Sales = 
    TOTALYTD(SUM(Housing[purchase_price]), Housing[date].[Date])

🧠 Insights

• Easily compare how regions perform in terms of revenue.
• Key Influencers AI visual uncovers price drivers like region, sqm, and house type.
• YTD sales gives a cumulative view and compares performance across years.
• Offer-to-SQM reveals how much buyers are offering per square meter.
• SQM Price trends help identify urban vs suburban areas.

📸 Screenshot – Sales Performance

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📊 Page 3 – House Type Insights

🔍 Visuals

• Avg Offer & Purchase Price by House Type
• Avg Inflation / Interest Rate / Yield by House Type
• Avg SQM and SQM Price by House Type

🧮 DAX Measure

Average SQM Price = 
    AVERAGE(Housing[sqm_price])

🧠 Insights

• Highlights pricing trends by house type (e.g., detached, apartments).
• Yield and inflation by category inform better investment decisions.
• SQM & Price per SQM compare density and valuation by category.

📸 Screenshot – House Type Insights

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🛠️ Tools & Tech Stack

• Data Source: Google BigQuery (CSV)
• Visualization: Power BI Desktop
• Scripting: DAX
• AI Integration: Key Influencers (Power BI Visual)

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🚀 How to Use

1. Open the .pbix file in Power BI Desktop.
2. Navigate across the report pages to explore the data.
3. Use slicers for filtering:
   • Region
   • Sales Type
   • House Type
   • Year and Quarter