Property Market Correlogram

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np

np.random.seed(654)

n = 160
neighborhood = np.random.choice(['Downtown', 'Suburban', 'Rural'], n)
data = {
    'Price ($K)': np.where(neighborhood == 'Downtown', np.random.normal(800, 200, n),
                          np.where(neighborhood == 'Suburban', np.random.normal(450, 100, n), np.random.normal(250, 80, n))),
    'Size (sqft)': np.where(neighborhood == 'Downtown', np.random.normal(1200, 300, n),
                           np.where(neighborhood == 'Suburban', np.random.normal(2200, 500, n), np.random.normal(2800, 600, n))),
    'Age (yrs)': np.where(neighborhood == 'Downtown', np.random.normal(50, 20, n),
                         np.where(neighborhood == 'Suburban', np.random.normal(25, 10, n), np.random.normal(15, 8, n))),
    'Lot (acres)': np.where(neighborhood == 'Rural', np.random.normal(2, 1, n),
                           np.where(neighborhood == 'Suburban', np.random.normal(0.3, 0.1, n), np.random.normal(0.05, 0.02, n))),
    'Area': neighborhood
}
df = pd.DataFrame(data)

sns.set_style("whitegrid", {'axes.facecolor': '#ffffff', 'figure.facecolor': '#ffffff', 'grid.color': '#eeeeee'})

palette = {'Downtown': '#C82909', 'Suburban': '#27D3F5', 'Rural': '#6CF527'}

g = sns.pairplot(
    df,
    hue='Area',
    palette=palette,
    height=2,
    kind='scatter',
    diag_kind='hist',
    markers=['s', 'o', '^'],
    plot_kws={'alpha': 0.7, 's': 55, 'edgecolor': 'white', 'linewidths': 0.6},
    diag_kws={'alpha': 0.6, 'edgecolor': 'white', 'linewidth': 0.5}
)

g.fig.set_facecolor('#ffffff')
for ax in g.axes.flat:
    if ax:
        ax.set_facecolor('#ffffff')
        for spine in ax.spines.values():
            spine.set_color('#dddddd')
        ax.tick_params(colors='#666666')
        ax.xaxis.label.set_color('#333333')
        ax.yaxis.label.set_color('#333333')

g.fig.suptitle('Property Market Analysis', fontsize=14, fontweight='bold', color='#1a1a1a', y=1.02)

plt.tight_layout()
plt.show()