🏨 Dynamic Hotel Pricing Optimization

📉 03_Price Elasticity Modeling

Goal: Model how booking demand responds to changes in price, and estimate optimal pricing zones.

Code 
# 1️⃣ Load Data
import pandas as pd

df = pd.read_csv('../data/raw/hotel_bookings.csv')

# Filter to completed bookings only (exclude cancellations)
df = df[df['is_canceled'] == 0]

df.head()
hotel is_canceled lead_time arrival_date_year arrival_date_month arrival_date_week_number arrival_date_day_of_month stays_in_weekend_nights stays_in_week_nights adults ... deposit_type agent company days_in_waiting_list customer_type adr required_car_parking_spaces total_of_special_requests reservation_status reservation_status_date
0 Resort Hotel 0 342 2015 July 27 1 0 0 2 ... No Deposit NaN NaN 0 Transient 0.0 0 0 Check-Out 2015-07-01
1 Resort Hotel 0 737 2015 July 27 1 0 0 2 ... No Deposit NaN NaN 0 Transient 0.0 0 0 Check-Out 2015-07-01
2 Resort Hotel 0 7 2015 July 27 1 0 1 1 ... No Deposit NaN NaN 0 Transient 75.0 0 0 Check-Out 2015-07-02
3 Resort Hotel 0 13 2015 July 27 1 0 1 1 ... No Deposit 304.0 NaN 0 Transient 75.0 0 0 Check-Out 2015-07-02
4 Resort Hotel 0 14 2015 July 27 1 0 2 2 ... No Deposit 240.0 NaN 0 Transient 98.0 0 1 Check-Out 2015-07-03

5 rows × 32 columns

Code 
# 2️⃣ Aggregate Price & Demand

# Group by price buckets → example simple binning
df['price_bucket'] = pd.cut(df['adr'], bins=[0, 50, 100, 150, 200, 300, 500, 1000])

# Count bookings per price bucket
demand_by_price = df.groupby('price_bucket').size().reset_index(name='bookings')

demand_by_price
C:\Users\atima\AppData\Local\Temp\ipykernel_4156\1096299440.py:7: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  demand_by_price = df.groupby('price_bucket').size().reset_index(name='bookings')
price_bucket bookings
0 (0, 50] 7706
1 (50, 100] 33972
2 (100, 150] 21520
3 (150, 200] 7184
4 (200, 300] 2848
5 (300, 500] 187
6 (500, 1000] 2
Code 
# 3️⃣ Plot Demand vs Price

import matplotlib.pyplot as plt

# Midpoint of each bucket → simple estimate
demand_by_price['price_mid'] = demand_by_price['price_bucket'].apply(lambda x: x.mid)

plt.figure(figsize=(8,6))
plt.plot(demand_by_price['price_mid'], demand_by_price['bookings'], marker='o')
plt.title('Bookings vs Price (Elasticity Curve)')
plt.xlabel('Price (Midpoint of Bucket)')
plt.ylabel('Number of Bookings')
plt.grid()
plt.show()

Code 
# 4️⃣ Log-Log Regression (Elasticity Estimate)

import numpy as np
import statsmodels.api as sm

# Prepare log-log data
demand_by_price['price_mid'] = demand_by_price['price_mid'].astype(float)
X = np.log(demand_by_price['price_mid'])
y = np.log(demand_by_price['bookings'])

X = sm.add_constant(X)  # add intercept
model = sm.OLS(y, X).fit()

print(model.summary())

# Elasticity ≈ slope coefficient → % change in demand per % change in price
                            OLS Regression Results                            
==============================================================================
Dep. Variable:               bookings   R-squared:                       0.598
Model:                            OLS   Adj. R-squared:                  0.518
Method:                 Least Squares   F-statistic:                     7.441
Date:                Mon, 09 Jun 2025   Prob (F-statistic):             0.0414
Time:                        01:51:40   Log-Likelihood:                -14.821
No. Observations:                   7   AIC:                             33.64
Df Residuals:                       5   BIC:                             33.53
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
const         19.5084      4.513      4.323      0.008       7.909      31.108
price_mid     -2.3678      0.868     -2.728      0.041      -4.599      -0.137
==============================================================================
Omnibus:                          nan   Durbin-Watson:                   1.033
Prob(Omnibus):                    nan   Jarque-Bera (JB):                1.053
Skew:                          -0.709   Prob(JB):                        0.591
Kurtosis:                       1.734   Cond. No.                         27.0
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
c:\GitHub\DynamicHotelPricingOptimization\venv\Lib\site-packages\statsmodels\stats\stattools.py:74: ValueWarning: omni_normtest is not valid with less than 8 observations; 7 samples were given.
  warn("omni_normtest is not valid with less than 8 observations; %i "
Code 
# 5️⃣ Visualize Fitted Elasticity Curve

y_pred = model.predict(X)

plt.figure(figsize=(8,6))
plt.scatter(np.log(demand_by_price['price_mid']), np.log(demand_by_price['bookings']), label='Observed')
plt.plot(np.log(demand_by_price['price_mid']), y_pred, color='red', label='Fitted Line')
plt.title('Log-Log Elasticity Model')
plt.xlabel('log(Price)')
plt.ylabel('log(Bookings)')
plt.legend()
plt.grid()
plt.show()