Real Estate
EXECUTIVE DASHBOARD · REAL ESTATE

Ames Housing — Market Overview

A management-friendly view of pricing, segmentation, key drivers and geographic distribution. Use filters to focus on a specific price segment and quickly understand what moves value.

KPIs Segments Drivers Geography
Filters

Filters

Select a driver (numeric variable) and optionally restrict the scope by price segment.

Driver: Bedrooms Segment: All price ranges
Used in the “Drivers of price” section (correlation + simple model signal).
Use to compare behaviour inside a segment.
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Summary

Executive summary

Key takeaways for management — quick interpretation of the selected scope.

Summary
  • Typical price (median): 3 · Median is preferred for decisions when the market has premium outliers.
  • Dispersion (IQR): 1 · Higher IQR indicates wider pricing variety within the segment.
  • Driver signal (|r|): 0.292 · A stronger association suggests a more actionable pricing/valuation driver.
  • Data volume: 20538 observations included in the current scope.
KPIs
Sample size
20538
Properties in scope
Typical price (median)
3
Robust benchmark
Average price
3
Sensitive to premium outliers
Price dispersion (IQR)
1
Middle 50% spread
Min / Max
0 → 33
Range
Volatility (Std. dev.)
1
General variability
Skewness
2.13
Premium tail signal
Kurtosis
54.88
Outlier pressure

Drivers of price

Relationship between Price and Bedrooms.

Drivers
Correlation (Pearson) i
r: 0.292
p: 0.000000
Reliable association in this scope.
Explained variance (R²) i
R²: 0.085
β₁: 77476.73
Driver is a statistically significant predictor of price.
Practical use: prioritize drivers with stronger signals to guide valuation rules, pricing strategy, and segment targeting.

Market stability

Quick signals for outliers and “average reliability”.

Quality
Normality signal i
p: 0.000000
Non-normal behaviour: prefer robust metrics (median, IQR).
Outlier pressure i
skew: 2.13
kurt: 54.88
Higher values typically mean stronger premium segments and more extremes.
Segment differentiation i
p: 0.000000
Segments behave differently — good candidate for segmentation strategy.

Distribution — Bedrooms

Typical range and concentration of values.

Outliers and spread

Extremes + middle 50% in a single view.

Price vs Bedrooms

Visual relationship between the driver and price.

Bedrooms by segment

Segment-based policy view.

Correlation matrix

“What moves with what” view to spot drivers and redundancy.

Price vs Year Built

Appreciation patterns and vintage effects.

Geographic distribution

Spatial market view of the current scope.