King County Real Estate Market Analysis: Comprehensive Property Valuation Study¶

By Mohammad Sayem Chowdhury
Last Updated: June 13, 2025

Project Overview¶

This comprehensive real estate analytics project examines housing market dynamics in King County, Washington, including the greater Seattle metropolitan area. Using advanced statistical analysis and machine learning techniques, I investigate the key factors driving property values and develop predictive models for accurate home price estimation.

Market Analysis Period¶

Data Coverage: May 2014 - May 2015
Geographic Scope: King County, Washington (including Seattle)
Sample Size: 21,613 residential property sales

Research Objectives¶

  • Price Driver Analysis: Identify the most significant factors affecting residential property values
  • Market Segmentation: Analyze price patterns across different neighborhoods and property types
  • Predictive Modeling: Develop accurate models for property valuation and investment analysis
  • Investment Insights: Provide data-driven recommendations for real estate investment strategies

Author: Mohammad Sayem Chowdhury
Project Type: Real Estate Market Analytics
Domain: Property Valuation & Investment Analysis
Methodology: Statistical Analysis & Predictive Modeling

Table of Contents¶

  1. Market Data Overview & Import
  2. Property Feature Analysis
  3. Geographic Price Distribution
  4. Predictive Model Development
  5. Investment Insights & Recommendations

Executive Summary¶

King County Real Estate Market Dynamics¶

This analysis focuses on understanding the complex factors that drive residential property values in one of the most dynamic real estate markets in the United States. King County, home to Seattle and major technology companies, presents unique opportunities for data-driven real estate insights.

Key Research Questions¶

  • Which property characteristics have the strongest correlation with sale prices?
  • How do location factors (geography, proximity to amenities) influence valuation?
  • What predictive models can most accurately estimate property values?
  • Which areas present the best investment opportunities based on historical data?

Market Context: The 2014-2015 period represents a critical time in the Seattle real estate market, with significant growth driven by the technology sector expansion.

Dataset Features¶

  • id: Unique identifier for a house
  • date: Date house was sold
  • price: Sale price (target variable)
  • bedrooms: Number of bedrooms
  • bathrooms: Number of bathrooms
  • sqft_living: Living area square footage
  • sqft_lot: Lot size in square feet
  • floors: Number of floors
  • waterfront: Whether the house has a waterfront view
  • view: Number of times viewed
  • condition: Overall condition rating
  • grade: Overall grade based on King County grading system
  • sqft_above: Square footage above ground
  • sqft_basement: Basement square footage
  • yr_built: Year built
  • yr_renovated: Year renovated
  • zipcode: Zip code
  • lat: Latitude
  • long: Longitude

Notebook by Mohammad Sayem Chowdhury

You will require the following libraries:

In [4]:
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler,PolynomialFeatures
from sklearn.linear_model import LinearRegression
%matplotlib inline

Module 1: Importing Data Sets¶

Load the csv:

In [5]:
file_name='https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DA0101EN/coursera/project/kc_house_data_NaN.csv'
df=pd.read_csv(file_name)

We use the method head to display the first 5 columns of the dataframe.

In [6]:
df.head()
Out[6]:
Unnamed: 0 id date price bedrooms bathrooms sqft_living sqft_lot floors waterfront ... grade sqft_above sqft_basement yr_built yr_renovated zipcode lat long sqft_living15 sqft_lot15
0 0 7129300520 20141013T000000 221900.0 3.0 1.00 1180 5650 1.0 0 ... 7 1180 0 1955 0 98178 47.5112 -122.257 1340 5650
1 1 6414100192 20141209T000000 538000.0 3.0 2.25 2570 7242 2.0 0 ... 7 2170 400 1951 1991 98125 47.7210 -122.319 1690 7639
2 2 5631500400 20150225T000000 180000.0 2.0 1.00 770 10000 1.0 0 ... 6 770 0 1933 0 98028 47.7379 -122.233 2720 8062
3 3 2487200875 20141209T000000 604000.0 4.0 3.00 1960 5000 1.0 0 ... 7 1050 910 1965 0 98136 47.5208 -122.393 1360 5000
4 4 1954400510 20150218T000000 510000.0 3.0 2.00 1680 8080 1.0 0 ... 8 1680 0 1987 0 98074 47.6168 -122.045 1800 7503

5 rows × 22 columns

Question 1¶

Display the data types of each column using the attribute dtype, then take a screenshot and submit it, include your code in the image.

In [7]:
print(df.dtypes)

Unnamed: 0         int64
id                 int64
date              object
price            float64
bedrooms         float64
bathrooms        float64
sqft_living        int64
sqft_lot           int64
floors           float64
waterfront         int64
view               int64
condition          int64
grade              int64
sqft_above         int64
sqft_basement      int64
yr_built           int64
yr_renovated       int64
zipcode            int64
lat              float64
long             float64
sqft_living15      int64
sqft_lot15         int64
dtype: object

We use the method describe to obtain a statistical summary of the dataframe.

In [8]:
df.describe()
Out[8]:
Unnamed: 0 id price bedrooms bathrooms sqft_living sqft_lot floors waterfront view ... grade sqft_above sqft_basement yr_built yr_renovated zipcode lat long sqft_living15 sqft_lot15
count 21613.00000 2.161300e+04 2.161300e+04 21600.000000 21603.000000 21613.000000 2.161300e+04 21613.000000 21613.000000 21613.000000 ... 21613.000000 21613.000000 21613.000000 21613.000000 21613.000000 21613.000000 21613.000000 21613.000000 21613.000000 21613.000000
mean 10806.00000 4.580302e+09 5.400881e+05 3.372870 2.115736 2079.899736 1.510697e+04 1.494309 0.007542 0.234303 ... 7.656873 1788.390691 291.509045 1971.005136 84.402258 98077.939805 47.560053 -122.213896 1986.552492 12768.455652
std 6239.28002 2.876566e+09 3.671272e+05 0.926657 0.768996 918.440897 4.142051e+04 0.539989 0.086517 0.766318 ... 1.175459 828.090978 442.575043 29.373411 401.679240 53.505026 0.138564 0.140828 685.391304 27304.179631
min 0.00000 1.000102e+06 7.500000e+04 1.000000 0.500000 290.000000 5.200000e+02 1.000000 0.000000 0.000000 ... 1.000000 290.000000 0.000000 1900.000000 0.000000 98001.000000 47.155900 -122.519000 399.000000 651.000000
25% 5403.00000 2.123049e+09 3.219500e+05 3.000000 1.750000 1427.000000 5.040000e+03 1.000000 0.000000 0.000000 ... 7.000000 1190.000000 0.000000 1951.000000 0.000000 98033.000000 47.471000 -122.328000 1490.000000 5100.000000
50% 10806.00000 3.904930e+09 4.500000e+05 3.000000 2.250000 1910.000000 7.618000e+03 1.500000 0.000000 0.000000 ... 7.000000 1560.000000 0.000000 1975.000000 0.000000 98065.000000 47.571800 -122.230000 1840.000000 7620.000000
75% 16209.00000 7.308900e+09 6.450000e+05 4.000000 2.500000 2550.000000 1.068800e+04 2.000000 0.000000 0.000000 ... 8.000000 2210.000000 560.000000 1997.000000 0.000000 98118.000000 47.678000 -122.125000 2360.000000 10083.000000
max 21612.00000 9.900000e+09 7.700000e+06 33.000000 8.000000 13540.000000 1.651359e+06 3.500000 1.000000 4.000000 ... 13.000000 9410.000000 4820.000000 2015.000000 2015.000000 98199.000000 47.777600 -121.315000 6210.000000 871200.000000

8 rows × 21 columns

Module 2: Data Wrangling¶

Question 2¶

Drop the columns "id" and "Unnamed: 0" from axis 1 using the method drop(), then use the method describe() to obtain a statistical summary of the data. Take a screenshot and submit it, make sure the inplace parameter is set to True

In [9]:
df.drop(['id','Unnamed: 0'],axis=1,inplace=True)
In [10]:
df.describe(include = "all")
Out[10]:
date price bedrooms bathrooms sqft_living sqft_lot floors waterfront view condition grade sqft_above sqft_basement yr_built yr_renovated zipcode lat long sqft_living15 sqft_lot15
count 21613 2.161300e+04 21600.000000 21603.000000 21613.000000 2.161300e+04 21613.000000 21613.000000 21613.000000 21613.000000 21613.000000 21613.000000 21613.000000 21613.000000 21613.000000 21613.000000 21613.000000 21613.000000 21613.000000 21613.000000
unique 372 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
top 20140623T000000 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
freq 142 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
mean NaN 5.400881e+05 3.372870 2.115736 2079.899736 1.510697e+04 1.494309 0.007542 0.234303 3.409430 7.656873 1788.390691 291.509045 1971.005136 84.402258 98077.939805 47.560053 -122.213896 1986.552492 12768.455652
std NaN 3.671272e+05 0.926657 0.768996 918.440897 4.142051e+04 0.539989 0.086517 0.766318 0.650743 1.175459 828.090978 442.575043 29.373411 401.679240 53.505026 0.138564 0.140828 685.391304 27304.179631
min NaN 7.500000e+04 1.000000 0.500000 290.000000 5.200000e+02 1.000000 0.000000 0.000000 1.000000 1.000000 290.000000 0.000000 1900.000000 0.000000 98001.000000 47.155900 -122.519000 399.000000 651.000000
25% NaN 3.219500e+05 3.000000 1.750000 1427.000000 5.040000e+03 1.000000 0.000000 0.000000 3.000000 7.000000 1190.000000 0.000000 1951.000000 0.000000 98033.000000 47.471000 -122.328000 1490.000000 5100.000000
50% NaN 4.500000e+05 3.000000 2.250000 1910.000000 7.618000e+03 1.500000 0.000000 0.000000 3.000000 7.000000 1560.000000 0.000000 1975.000000 0.000000 98065.000000 47.571800 -122.230000 1840.000000 7620.000000
75% NaN 6.450000e+05 4.000000 2.500000 2550.000000 1.068800e+04 2.000000 0.000000 0.000000 4.000000 8.000000 2210.000000 560.000000 1997.000000 0.000000 98118.000000 47.678000 -122.125000 2360.000000 10083.000000
max NaN 7.700000e+06 33.000000 8.000000 13540.000000 1.651359e+06 3.500000 1.000000 4.000000 5.000000 13.000000 9410.000000 4820.000000 2015.000000 2015.000000 98199.000000 47.777600 -121.315000 6210.000000 871200.000000

We can see we have missing values for the columns bedrooms and bathrooms

In [11]:
print("number of NaN values for the column bedrooms :", df['bedrooms'].isnull().sum())
print("number of NaN values for the column bathrooms :", df['bathrooms'].isnull().sum())

number of NaN values for the column bedrooms : 13
number of NaN values for the column bathrooms : 10

We can replace the missing values of the column 'bedrooms' with the mean of the column 'bedrooms' using the method replace(). Don't forget to set the inplace parameter to True

In [12]:
mean=df['bedrooms'].mean()
df['bedrooms'].replace(np.nan,mean, inplace=True)

We also replace the missing values of the column 'bathrooms' with the mean of the column 'bathrooms' using the method replace(). Don't forget to set the inplace parameter top True

In [13]:
mean=df['bathrooms'].mean()
df['bathrooms'].replace(np.nan,mean, inplace=True)
In [14]:
print("number of NaN values for the column bedrooms :", df['bedrooms'].isnull().sum())
print("number of NaN values for the column bathrooms :", df['bathrooms'].isnull().sum())

number of NaN values for the column bedrooms : 0
number of NaN values for the column bathrooms : 0

Module 3: Exploratory Data Analysis¶

Question 3¶

Use the method value_counts to count the number of houses with unique floor values, use the method .to_frame() to convert it to a dataframe.

In [15]:
df['floors'].value_counts()
Out[15]:
1.0    10680
2.0     8241
1.5     1910
3.0      613
2.5      161
3.5        8
Name: floors, dtype: int64
In [16]:
df['floors'].value_counts().to_frame()
Out[16]:
floors
1.0 10680
2.0 8241
1.5 1910
3.0 613
2.5 161
3.5 8

Question 4¶

Use the function boxplot in the seaborn library to determine whether houses with a waterfront view or without a waterfront view have more price outliers.

In [17]:
sns.boxplot(x="waterfront", y="price", data=df)
Out[17]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc8e71d32d0>
No description has been provided for this image

Question 5¶

Use the function regplot in the seaborn library to determine if the feature sqft_above is negatively or positively correlated with price.

In [18]:
sns.regplot(x="price", y="sqft_above", data=df)
Out[18]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fc8e66e1790>
No description has been provided for this image

We can use the Pandas method corr() to find the feature other than price that is most correlated with price.

In [19]:
df.corr()['price'].sort_values()
Out[19]:
zipcode         -0.053203
long             0.021626
condition        0.036362
yr_built         0.054012
sqft_lot15       0.082447
sqft_lot         0.089661
yr_renovated     0.126434
floors           0.256794
waterfront       0.266369
lat              0.307003
bedrooms         0.308797
sqft_basement    0.323816
view             0.397293
bathrooms        0.525738
sqft_living15    0.585379
sqft_above       0.605567
grade            0.667434
sqft_living      0.702035
price            1.000000
Name: price, dtype: float64

Module 4: Model Development¶

We can Fit a linear regression model using the longitude feature 'long' and caculate the R^2.

In [20]:
X = df[['long']]
Y = df['price']
lm = LinearRegression()
lm.fit(X,Y)
lm.score(X, Y)
Out[20]:
0.00046769430149007363

Question 6¶

Fit a linear regression model to predict the 'price' using the feature 'sqft_living' then calculate the R^2. Take a screenshot of your code and the value of the R^2.

In [27]:
X = df[['sqft_living']]
Y = df['price']
lm1 = LinearRegression()
lm1.fit(X,Y)
lm1.score(X, Y)
Out[27]:
0.4928532179037931

Question 7¶

Fit a linear regression model to predict the 'price' using the list of features:

In [30]:
features =["floors", "waterfront","lat" ,"bedrooms" ,"sqft_basement" ,"view" ,"bathrooms","sqft_living15","sqft_above","grade","sqft_living"]

Then calculate the R^2. Take a screenshot of your code.

In [34]:
X = df[features]
Y = df['price']
lm2 = LinearRegression()
lm2.fit(X, Y)
lm2.score(X, Y)
Out[34]:
0.657679183672129

This will help with Question 8¶

Create a list of tuples, the first element in the tuple contains the name of the estimator:

'scale'

'polynomial'

'model'

The second element in the tuple contains the model constructor

StandardScaler()

PolynomialFeatures(include_bias=False)

LinearRegression()

In [35]:
Input=[('scale',StandardScaler()),('polynomial', PolynomialFeatures(include_bias=False)),('model',LinearRegression())]

Question 8¶

Use the list to create a pipeline object to predict the 'price', fit the object using the features in the list features, and calculate the R^2.

In [39]:
pipe=Pipeline(Input)
pipe.fit(X,Y)
pipe.score(X,Y)
Out[39]:
0.7513408553309376

Module 5: Model Evaluation and Refinement¶

Import the necessary modules:

In [40]:
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
print("done")

done

We will split the data into training and testing sets:

In [41]:
features =["floors", "waterfront","lat" ,"bedrooms" ,"sqft_basement" ,"view" ,"bathrooms","sqft_living15","sqft_above","grade","sqft_living"]    
X = df[features]
Y = df['price']

x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.15, random_state=1)


print("number of test samples:", x_test.shape[0])
print("number of training samples:",x_train.shape[0])

number of test samples: 3242
number of training samples: 18371

Question 9¶

Create and fit a Ridge regression object using the training data, set the regularization parameter to 0.1, and calculate the R^2 using the test data.

In [42]:
from sklearn.linear_model import Ridge
In [47]:
RigeModel=Ridge(alpha=0.1)
RigeModel.fit(x_train, y_train)
RigeModel.score(x_test, y_test)
Out[47]:
0.6478759163939122

Question 10¶

Perform a second order polynomial transform on both the training data and testing data. Create and fit a Ridge regression object using the training data, set the regularisation parameter to 0.1, and calculate the R^2 utilising the test data provided. Take a screenshot of your code and the R^2.

In [48]:
pr1=PolynomialFeatures(degree=2)

x_train_pr1=pr1.fit_transform(x_train)
x_test_pr1=pr1.fit_transform(x_test)
RigeModel=Ridge(alpha=0.1)
RigeModel.fit(x_train_pr1, y_train)
RigeModel.score(x_test_pr1, y_test)
Out[48]:
0.7002744279896707