Upload app.py

app.py

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+
+import streamlit as st
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+import numpy_financial as npf
+import pandas as pd
+from streamlit_folium import folium_static
+import leafmap.foliumap as leafmap
+import folium
+from shapely.geometry import Point, Polygon
+import geopandas
+import geopy
+from geopy.geocoders import Nominatim
+from geopy.extra.rate_limiter import RateLimiter
+from scipy.spatial import cKDTree
+
+#-----------------------------------------
+# Set page settings
+st.set_page_config(layout="wide")
+
+#-----------------------------------------
+# Sidebar
+with st.sidebar:
+  st.header('Welcome to the Airbnb Investment Tool!')
+  nav = st.selectbox('Navigation', ['Heuristic Pricing', 
+                                    'Investment Analysis',
+                                    'Customer View'])
+
+#-----------------------------------------
+# Additional Functions
+
+def p_title(title):
+    st.markdown(f'<h3 style="text-align: left; color:#F63366; font-size:28px;">{title}</h3>', unsafe_allow_html=True)
+
+
+# Function to return a GeoPandas DataFrame containing the listings
+# that are within a specified radius from a specified lat, long.
+def getNearbyListings(gdf_proj, input_long, input_lat, radius):
+  # Build Tree
+  airbnbCoords = np.array(list(gdf_proj.geometry.apply(lambda x: (x.x, x.y))))
+  airbnbTree = cKDTree(airbnbCoords)
+
+  # Convert lat-long to projected coords
+  gdf_input = geopandas.GeoSeries.from_xy(x=[input_long], y=[input_lat], crs=4326)
+  gdf_input_proj = gdf_input.to_crs(crs=32634)
+
+  coords = np.array(list((gdf_input_proj.x[0], gdf_input_proj.y[0])))
+
+  # Returns list of indices whose distance is <= radius
+  neighbours_indices = airbnbTree.query_ball_point(coords, radius)
+  gdf_neighbours_proj = gdf_proj.iloc[neighbours_indices, :]
+  gdf_neighbours = gdf_neighbours_proj.to_crs(crs=4326)
+
+  return gdf_neighbours
+
+# Function to return IRR.
+# Financial Modelling Tool.
+def investment_tool(house_price, loan_amount, loan_period, percentage_loan_interest_annual, 
+                    rental_charged_monthly, percentage_rental_tax, percentage_increase_in_rental_yearly, utilisation_rate, 
+                    yearly_refurbishment_costs, percentage_increase_in_refurbishment_yearly, ending_value_of_house):
+    
+  #expected format of percentage parameters is whole number and not decimals i.e., 5 instead of 0.05
+  #all non-% parameters are expected to be positive
+
+  house_price = int(house_price)
+  loan_amount = int(loan_amount)
+  loan_period = int(loan_period)
+  percentage_loan_interest_annual = int(percentage_loan_interest_annual)
+  rental_charged_monthly = int(rental_charged_monthly)
+  percentage_rental_tax = int(percentage_rental_tax)
+  percentage_increase_in_rental_yearly = int(percentage_increase_in_rental_yearly)
+  percentage_utilisation_rate = int(utilisation_rate)
+  yearly_refurbishment_costs = int(yearly_refurbishment_costs)
+  percentage_increase_in_refurbishment_yearly = int(percentage_increase_in_refurbishment_yearly)
+  ending_value_of_house = int(ending_value_of_house)
+
+  #ensuring the figures make sense
+  if loan_amount > house_price:
+    return print("Loan Amount cannot exceed House Price")
+
+  #creating the list of cash flows to be used to calculate internal rate of return
+  initial_cashflow = -(1 - loan_amount/house_price) * house_price
+  cashflow_list = [initial_cashflow]
+
+  #finding the annual mortgage assuming equal amortization
+  mortgage = npf.pmt(percentage_loan_interest_annual / 100, loan_period,  loan_amount) #the np.pmt function will automatically put mortgage as a negative cashflow
+
+  #finding the annual cashflows & loan balance changes during the loan period and appending them to the respective lists
+  for i in range(loan_period):
+    rental = 12 * rental_charged_monthly * ((1 + (percentage_increase_in_rental_yearly / 100)) ** i) * (1 - (percentage_rental_tax / 100)) * utilisation_rate / 100
+    refurbishment_cost = -1 * yearly_refurbishment_costs * ((1 + (percentage_increase_in_refurbishment_yearly / 100)) ** i)
+    
+    #the condition here is to include the salvage/ending value of the house to cashflows after loan repayments are finished
+    if i == (loan_period-1):
+      yearly_cashflow = ending_value_of_house + rental + mortgage + refurbishment_cost
+    else:
+      yearly_cashflow = rental + mortgage + refurbishment_cost
+    cashflow_list.append(yearly_cashflow)
+  
+  #finding the internal rate of return
+  irr = round(npf.irr(cashflow_list), 4)
+
+  #-----------------------------------
+  #Dataframe for plotting of graph
+  loan_dict = {'Year': [0], 'Starting Loan Balance': [0], 'Cumulative Interest Paid': [0], 'Cumulative Principal Paid': [0], 'Remaining Loan Balance': [0]}
+    
+  # Create DataFrame  
+  loan_dataframe = pd.DataFrame(loan_dict)
+    
+  #finding the annual mortgage assuming equal amortization
+  mortgage = npf.pmt(percentage_loan_interest_annual / 100, loan_period,  loan_amount) #the np.pmt function will automatically put mortgage as a negative cashflow
+
+  #updating the global dataframe
+  loan_dataframe.loc[0,'Starting Loan Balance'] = loan_amount
+
+  for i in range(loan_period):
+    loan_dataframe.loc[i,'Year'] = i+1
+
+    #the condition here is to calculate principal and interest paid
+    if i == 0:
+      loan_dataframe.loc[i,'Cumulative Interest Paid'] = loan_dataframe.loc[i,'Starting Loan Balance'] * (percentage_loan_interest_annual / 100)
+      loan_dataframe.loc[i,'Cumulative Principal Paid'] = (-1 * mortgage) - (loan_dataframe.loc[i,'Starting Loan Balance'] * (percentage_loan_interest_annual / 100))
+    else:
+      loan_dataframe.loc[i,'Cumulative Interest Paid'] = loan_dataframe.loc[i,'Starting Loan Balance'] * (percentage_loan_interest_annual / 100) + loan_dataframe.loc[i-1,'Cumulative Interest Paid']
+      loan_dataframe.loc[i,'Cumulative Principal Paid'] = (-1 * mortgage) - (loan_dataframe.loc[i,'Starting Loan Balance'] * (percentage_loan_interest_annual / 100)) + loan_dataframe.loc[i-1,'Cumulative Principal Paid']
+          
+    loan_dataframe.loc[i,'Remaining Loan Balance'] = loan_dataframe.loc[i,'Starting Loan Balance'] + (loan_dataframe.loc[i,'Starting Loan Balance'] * (percentage_loan_interest_annual / 100)) + mortgage
+      
+      #condition to update starting loan balance
+    if i != loan_period-1:
+      loan_dataframe.loc[i+1,'Starting Loan Balance'] = loan_dataframe.loc[i,'Remaining Loan Balance']
+
+  loan_dataframe['Remaining Loan Balance'] = pd.to_numeric(loan_dataframe['Remaining Loan Balance'])
+ 
+  return irr, loan_dataframe
+
+#-----------------------------------------
+# Load Airbnb listings data
+df_raw = pd.read_csv("data/listings_sf_withamenities.csv")
+df = df_raw.copy()
+gdf = geopandas.GeoDataFrame(
+    df, 
+    geometry=geopandas.points_from_xy(df.longitude, df.latitude),
+    crs=4326)
+gdf_proj = gdf.to_crs(crs=32634)
+
+#-----------------------------------------
+# Tab 1: Heuristic Pricing
+if nav == 'Heuristic Pricing':
+    
+    st.markdown("<h3 style='text-align: center; color:grey;'>Airbnb &#127968;</h3>", unsafe_allow_html=True)
+    st.text('')
+    p_title('Heuristic Pricing')
+    st.text('')
+
+    # Get address inputs
+    st.caption('Enter your address:')
+    with st.form("heuristics_form"):
+      col1, col2 = st.columns(2)
+      with col1:
+        postalcode = st.text_input("Postal Code", "94109")
+        street = st.text_input("Street", "1788 Clay Street")
+        city = st.selectbox("City", ["San Francisco"])
+      with col2:
+        state = st.selectbox("State", ["California"])
+        country = st.selectbox("Country", ["United States"])
+        radius = st.slider("Distance of nearest listings (metres)", min_value=500, max_value=2000, value=500, step=500)
+      submitted = st.form_submit_button("Submit")
+
+      if submitted:
+        # Get geolocation
+        geolocator = Nominatim(user_agent="GTA Lookup")
+        geocode = RateLimiter(geolocator.geocode, min_delay_seconds=1)
+        location = geolocator.geocode({"postalcode": postalcode, "street": street, "city": city, "state": state, "country": country})
+
+        # If the search address yields no result, set to default coords of San Fran
+        if location is None:
+          lat = 37.773972
+          lon = -122.431297
+          st.error("Address is not found. Please try again.")
+        else:
+          lat = location.latitude
+          lon = location.longitude
+
+          # Compute Stats
+          st.markdown('___')
+          st.caption('Recommended Pricing:')
+          gdf_nearby_listings = getNearbyListings(gdf_proj, lon, lat, radius=radius)
+          if len(gdf_nearby_listings) == 0:
+            st.error("There are no nearby listings.")
+          else:
+            col3, col4 = st.columns(2)
+            with col3:
+              df_nearby_stats = gdf_nearby_listings[["price"]].describe()
+              st.table(df_nearby_stats)
+            with col4:
+              # Plot Stats
+              fig = plt.figure(figsize=(10, 4))
+              sns.boxplot(x="price", data=gdf_nearby_listings, showfliers=False)
+              st.pyplot(fig)
+
+            # Plot using leafmap. Responsive width.
+            m = leafmap.Map(tiles="OpenStreetMap", location=[lat, lon], zoom_start=15)
+            m.add_marker(location=[lat, lon])
+            m.add_points_from_xy(gdf_nearby_listings, x="longitude", y="latitude", 
+                     popup=["id", "price", "review_scores_rating"],
+                     color_options=['red'])
+            m.add_heatmap(data=gdf_nearby_listings, 
+                          latitude="latitude", longitude="longitude", 
+                          value="price", min_opacity=0.1,
+                          name="Price Heatmap", blue=50)
+            m.to_streamlit()
+
+#-----------------------------------------
+# Tab 2: Investment Analysis
+if nav == 'Investment Analysis':
+    st.markdown("<h3 style='text-align: center; color:grey;'>Airbnb &#127968;</h3>", unsafe_allow_html=True)
+    st.text('')
+    p_title('Investment Analysis')
+    
+    # Financial Projections
+    st.caption("Enter data here")
+    with st.form("investment_form"):
+      col1_2, col2_2, col3_2 = st.columns(3)
+      with col1_2:
+        house_price = st.number_input("Purchase Price of House ($)", min_value=0, value=250000)
+        loan_amount = st.number_input("Loan Amount ($)", min_value=0, value=150000)
+        loan_period = st.number_input("Loan Period (Years)", min_value=0, value=15)
+        percentage_loan_interest_annual = st.number_input("Annual Loan I/R (%)", min_value=0.0, max_value=100.0, value=2.1)
+      with col2_2:
+        rental_charged_monthly = st.number_input("Monthly Rental ($)", min_value=0, value=2000)
+        percentage_rental_tax = st.number_input("Rental Tax (%)", min_value=0.0, value=0.0)
+        percentage_increase_in_rental_yearly = st.number_input("Annual Rental Increase (%)", min_value=0.0, value=1.0)
+        utilisation_rate = st.number_input("Utilisation Rate (%)", min_value=0.0, value=50.0)
+      with col3_2:
+        yearly_refurbishment_costs = st.number_input("Yearly Refurbishment Costs ($)", min_value=0, value=3000)
+        percentage_increase_in_refurbishment_yearly = st.number_input("Yearly Refurbishment Costs Increase (%)", min_value=0.0, value=2.0)
+        ending_value_of_house = st.number_input("Ending Value of House ($)", min_value=0, value=300000)
+      submitted2 = st.form_submit_button("Submit")
+
+      if submitted2:
+        irr, loan_dataframe = investment_tool(house_price, loan_amount, loan_period, percentage_loan_interest_annual, 
+                rental_charged_monthly, percentage_rental_tax, percentage_increase_in_rental_yearly, utilisation_rate, 
+                yearly_refurbishment_costs, percentage_increase_in_refurbishment_yearly, ending_value_of_house)
+        st.markdown('___')
+        st.caption("Expected Internal Rate of Return")
+        st.text("{:.2%}".format(irr))
+        # Print plots
+        fig = plt.figure(figsize=(10, 4))
+        plt.bar(loan_dataframe['Year'], loan_dataframe['Cumulative Principal Paid'], color='lightcoral')
+        plt.bar(loan_dataframe['Year'], loan_dataframe['Cumulative Interest Paid'], bottom=loan_dataframe['Cumulative Principal Paid'], color='lightsalmon')
+        plt.plot(loan_dataframe['Year'], loan_dataframe['Remaining Loan Balance'], color='crimson')
+        plt.ylabel('Amount')
+        plt.title('Loan Balance')
+        plt.legend(('Loan Balance Remaining','Cumulative Principal Paid', 'Cumulative Interest Paid'))
+        st.pyplot(fig)
+
+if nav == "Customer View":
+    st.markdown("<h3 style='text-align: center; color:grey;'>Airbnb &#127968;</h3>", unsafe_allow_html=True)
+    st.text('')
+    p_title('Customer View')
+
+    customer_tableau_embed_code = "<div class='tableauPlaceholder' id='viz1650723513927' style='position: relative'><noscript><a href='#'><img alt=' ' src='https:&#47;&#47;public.tableau.com&#47;static&#47;images&#47;Da&#47;Dashboard_1_16505589498970&#47;EDA&#47;1_rss.png' style='border: none' /></a></noscript><object class='tableauViz'  style='display:none;'><param name='host_url' value='https%3A%2F%2Fpublic.tableau.com%2F' /> <param name='embed_code_version' value='3' /> <param name='site_root' value='' /><param name='name' value='Dashboard_1_16505589498970&#47;EDA' /><param name='tabs' value='yes' /><param name='toolbar' value='yes' /><param name='static_image' value='https:&#47;&#47;public.tableau.com&#47;static&#47;images&#47;Da&#47;Dashboard_1_16505589498970&#47;EDA&#47;1.png' /> <param name='animate_transition' value='yes' /><param name='display_static_image' value='yes' /><param name='display_spinner' value='yes' /><param name='display_overlay' value='yes' /><param name='display_count' value='yes' /><param name='language' value='en-US' /></object></div>                <script type='text/javascript'>                    var divElement = document.getElementById('viz1650723513927');                    var vizElement = divElement.getElementsByTagName('object')[0];                    if ( divElement.offsetWidth > 800 ) { vizElement.style.width='1600px';vizElement.style.height='1150px';} else if ( divElement.offsetWidth > 500 ) { vizElement.style.width='1600px';vizElement.style.height='1150px';} else { vizElement.style.width='100%';vizElement.style.height='2850px';}                     var scriptElement = document.createElement('script');                    scriptElement.src = 'https://public.tableau.com/javascripts/api/viz_v1.js';                    vizElement.parentNode.insertBefore(scriptElement, vizElement);                </script>"
+    st.components.v1.html(customer_tableau_embed_code, height=2000, scrolling=True)