A guide to leverage machine learning to build a rental price prediction model for Airbnb listings

Airbnb has revolutionized the travel industry by allowing hosts to rent out their properties or spare rooms to guests as an alternative to traditional hotels. Since its launch in 2008, Airbnb has grown massively – as of 2021, it has over 4 million hosts and has facilitated over 1 billion guest arrivals across more than 220 countries and regions.

One of the key challenges for Airbnb hosts is setting the right price for their rental listings. Price the listing too high and you may struggle to get bookings; price too low and you‘re leaving money on the table. This is where machine learning can help – by leveraging historical rental data, we can build predictive models to accurately forecast the optimal price for an Airbnb listing based on its attributes like location, type of property, amenities, and more.

In this post, we‘ll walk through the process of building a deep learning model using TensorFlow to predict Airbnb rental prices in New York City. We‘ll be using the NYC Airbnb Open Data dataset from Kaggle. By the end, you‘ll see how to implement an end-to-end machine learning pipeline from data loading and exploration to model building and evaluation. Let‘s dive in!

The Dataset

We‘ll be using the NYC Airbnb Open Data dataset from Kaggle. This dataset contains information on Airbnb listings in New York City, including:

• Listing details like price, neighborhood, room type, minimum nights stay, availability
• Review data like number of reviews and review scores
• Host details like response time, acceptance rate, listing count
• Geographic information including latitude and longitude

The dataset has nearly 50,000 listings with 16 columns in total. It provides a rich and realistic dataset for us to work with.

Loading and Exploring the Data

First, let‘s load the CSV data into a Pandas DataFrame:

import pandas as pd

df = pd.read_csv(‘AB_NYC_2019.csv‘)
df.head()

We can check the shape of the data and look for any missing values:

print(df.shape)
print(df.isnull().sum())

There are a few columns with a large number of missing values that we‘ll need to handle later.

Next, let‘s visualize the distribution of the price variable we want to predict:

import matplotlib.pyplot as plt
import seaborn as sns

plt.figure(figsize=(8,6))
sns.histplot(df.price)
plt.xlabel(‘Price ($)‘)
plt.title(‘Distribution of NYC Airbnb Rental Prices‘)
plt.show()

We can see the price distribution is heavily right-skewed, with a few very expensive listings. The majority of listings are under $500 per night.

Let‘s also examine the relationship between rental price and some of the key categorical features like room type, neighborhood, and property type:

plt.figure(figsize=(8,6))
sns.boxplot(x=‘room_type‘, y=‘price‘, data=df)
plt.xticks(rotation=45)
plt.title(‘Price vs Room Type‘)
plt.show()

sns.catplot(x=‘neighbourhood_group‘, y=‘price‘, kind=‘box‘, height=6, aspect=2, data=df)
plt.xticks(rotation=45)
plt.title(‘Price vs Neighborhood‘)
plt.show()

As expected, we see that factors like room type and neighborhood have a significant impact on rental price. Private rooms and shared rooms tend to be cheaper than entire homes/apartments. Manhattan has the highest rental prices on average compared to the outer boroughs.

Data Preprocessing

Now that we‘ve explored the data, let‘s preprocess it and get it ready for modeling. The key steps are:

1. Remove unneeded columns
2. Handle missing values
3. Encode categorical variables
4. Scale/normalize numeric features

columns_to_remove = [‘id‘, ‘name‘, ‘host_id‘, ‘host_name‘, ‘last_review‘] df = df.drop(columns_to_remove, axis=1)

df.reviews_per_month = df.reviews_per_month.fillna(0)

categorical_columns = [‘neighbourhood_group‘, ‘neighbourhood‘, ‘room_type‘]

for col in categorical_columns:
df[col] = df[col].astype(‘category‘)
df[col] = df[col].cat.codes

from sklearn.preprocessing import MinMaxScaler

numeric_columns = [‘minimum_nights‘, ‘number_of_reviews‘, ‘reviews_per_month‘,
‘calculated_host_listings_count‘, ‘availability_365‘]

scaler = MinMaxScaler()
df[numeric_columns] = scaler.fit_transform(df[numeric_columns])

Feature Engineering: Latitude-Longitude Cross

The geographic coordinates of the listing (latitude and longitude) are important features, but we can‘t just feed raw lat/long values directly into our model. Instead, we‘ll perform feature engineering to create a 2D grid over the lat/long space.

This allows the model to learn spatial relationships by treating nearby listings as being more similar to each other.

Here‘s how to construct the feature cross using TensorFlow:

import tensorflow as tf

def get_feature_cross(df):
lat_buckets = tf.feature_column.bucketized_column(
tf.feature_column.numeric_column(‘latitude‘),
boundaries=list(np.linspace(40.5, 40.95, 80))
)
long_buckets = tf.feature_column.bucketized_column(
tf.feature_column.numeric_column(‘longitude‘),
boundaries=list(np.linspace(-74.25, -73.65, 80))
)
return tf.feature_column.crossed_column([lat_buckets, long_buckets], 7000)

feature_cross = get_feature_cross(df)

We create a grid of 80×80 equally-spaced buckets spanning the range of latitude and longitude values. Then we cross the bucketized lat/long columns to produce the feature cross. This transforms the two original numeric features into a single high-dimensional categorical feature.

Building the Model

We‘re now ready to build our deep learning model using TensorFlow and Keras. We‘ll use the Keras Sequential API to stack layers.

Here is the code to build and compile the model:

def build_model():
model = tf.keras.Sequential([
tf.keras.layers.DenseFeatures([feature_cross]),
tf.keras.layers.Dense(128, activation=‘relu‘),
tf.keras.layers.Dropout(0.5),
tf.keras.layers.Dense(64, activation=‘relu‘),
tf.keras.layers.Dropout(0.5),
tf.keras.layers.Dense(1, activation=‘linear‘)
])

model.compile(
    loss=‘mse‘,
    optimizer=tf.keras.optimizers.Adam(learning_rate=0.005),
    metrics=[‘mae‘, tf.keras.metrics.RootMeanSquaredError()])

return model

model = build_model()

Our model architecture consists of:

• A DenseFeatures layer that accepts the latitude-longitude feature cross as input
• Two fully-connected Dense layers of size 128 and 64 with ReLU activations
• Dropout layers with rate 0.5 after each Dense layer to prevent overfitting
• A final output layer that predicts price

We compile the model using the Adam optimizer with a learning rate of 0.005, mean squared error loss, and monitor additional metrics like MAE and RMSE.

Training the Model

To train the model, we first need to create our input features and labels:

labels = df.pop(‘price‘)
features = df

Before training, we‘ll define a couple callbacks:

• EarlyStopping to halt training if validation loss stops improving
• ReduceLROnPlateau to reduce the learning rate if loss plateaus

early_stop = tf.keras.callbacks.EarlyStopping(monitor=‘val_loss‘, patience=10)
lr_reduce = tf.keras.callbacks.ReduceLROnPlateau(monitor=‘val_loss‘, factor=0.2, patience=5)

Now we can train the model using model.fit():

history = model.fit(
features, labels,
epochs=100, batch_size=128,
validation_split = 0.2,
callbacks=[early_stop, lr_reduce])

The model is trained for a maximum of 100 epochs with a batch size of 128, using 80% of the data for training and 20% for validation.

Evaluating Performance

After training, we can evaluate the model‘s performance on the test set and plot some diagnostic learning curves.

First, let‘s visualize the training progress:

plt.plot(history.history[‘loss‘], label=‘Training Loss‘)
plt.plot(history.history[‘val_loss‘], label=‘Validation Loss‘)
plt.xlabel(‘Epoch‘)
plt.ylabel(‘Loss‘)
plt.legend()
plt.show()

The training and validation loss curves can give insights into whether the model has overfit or underfit.

Now let‘s evaluate on the held-out test set:

loss, mae, rmse = model.evaluate(test_features, test_labels)
print(f‘Test RMSE: {rmse:.2f}, MAE: {mae:.2f}‘)

By tracking metrics like RMSE and MAE, we get a sense for how well the model generalizes to unseen data and how far off its predictions are on average from the true rental prices.

We can also visualize the model‘s predictions vs actual prices on the test set:

test_predictions = model.predict(test_features).flatten()

plt.figure(figsize=(8, 8))
plt.scatter(test_labels, test_predictions)
plt.xlabel(‘Actual Prices ($)‘)
plt.ylabel(‘Predicted Prices ($)‘)
plt.title(‘Actual vs Predicted Prices‘)
plt.plot([(0, 0), (1000, 1000)], [(0, 0), (1000, 1000)])
plt.show()

The scatter plot shows the correlation between true and predicted prices – the closer the points fall along the diagonal line, the more accurate the model‘s predictions are.

Future Work

There are a number of potential improvements we could make to take this model further:

• Experiment with additional features like amenities, host response rate, etc.
• Try other modeling approaches like XGBoost or LightGBM
• Conduct more systematic hyperparameter tuning using grid search or Bayesian optimization
• Explore methods to interpret the model and understand what features drive price

Overall, this post demonstrates how we can leverage machine learning to build a rental price prediction model for Airbnb listings. By understanding the key factors that influence price, Airbnb hosts can make more informed pricing decisions to maximize bookings and revenue. As Airbnb continues to grow and evolve, predictive modeling will become an increasingly valuable tool to navigate the short-term rental market.