Unlock insights with our dynamic Airbnb dashboard! Whether you're a property owner, a traveler, or a market analyst, our dashboard offers a comprehensive view of Airbnb listings and reviews in Boston.

• Interactive Map: Navigate through Boston’s neighborhoods to see the distribution of listings.
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This visually compelling dashboard provides an insightful representation of the population distribution across the United States. Designed for ease of use, it offers a comprehensive view of population estimates and year-over-year population changes by state.Define and store new regions on the map using this Astrato Data App. Interactive map allows users to zoom in and pan across different regions for a closer look.

Looking to leap into the UK property market? Discover your buying power with our intuitive Affordability Calculator, designed specifically for the UK housing landscape. Whether you're a first-time buyer or considering your next property adventure, our tool demystifies the numbers for you.Just enter your salary, savings, and monthly outgoings, and let our calculator do the rest. It will factor in the typical lending criteria, giving you an instant estimate of what you could afford. No need to wonder about mortgage multipliers or sift through your finances – we've streamlined the process.With our Affordability Calculator, embark on your property search with confidence, knowing exactly where you stand. Dive in, it's the first step to unlocking the door to your new home!👩‍💻 Scroll down to copy the function



create or replace function calculate_property_affordability(salary int, savings int, existing_property_value int, monthly_outgoings int, salary_multiplier float, is_first_time_buyer boolean) /* YOU MAY WISH TO CHANGE THE INPUT FIELD DATA TYPE, TO SUIT THE OUTPUT */

returns string

language python

runtime_version = 3.9

handler = 'calculate_property_affordability'

--packages = ('pandas','pandas')

as

$$



def calculate_property_affordability(salary, savings, existing_property_value=0, monthly_outgoings=0, salary_multiplier=4.45, is_first_time_buyer=False):

def stamp_duty(property_value):

if is_first_time_buyer:

thresholds = [425000, 925000, 1500000]

rates = [0.05, 0.10, 0.12]

else:

thresholds = [250000, 925000, 1500000]

rates = [0.05, 0.10, 0.12]



duty = 0

remaining_value = property_value



for i in range(len(thresholds)):

if remaining_value <= thresholds[i]:

break

taxable_value = min(remaining_value - thresholds[i], thresholds[i+1] - thresholds[i] if i+1 < len(thresholds) else float('inf')) duty += taxable_value * rates[i] remaining_value -= taxable_value if remaining_value > thresholds[-1]:

duty += (remaining_value - thresholds[-1]) * rates[-1]



return duty



loan_amount = salary * salary_multiplier

total_affordability = loan_amount + savings + existing_property_value - (monthly_outgoings * 12)



# Estimate property value by iteratively subtracting stamp duty until the value converges

estimated_value = total_affordability

while True:

sdlt = stamp_duty(estimated_value)

new_estimated_value = total_affordability - sdlt

if abs(new_estimated_value - estimated_value) < 1: # Convergence threshold

break

estimated_value = new_estimated_value



return estimated_value, stamp_duty(estimated_value)



$$;



The Seattle Housing Market Dashboard displays data for homes sold in Seattle, Washington, USA between August and December 2022. The dashboard showcases Astrato’s Input form functionality, and enables the user to predict a price of their desired home based on their requirements, using a sophisticated filtering option.The Price Prediction Sheet uses a statistical model to predict a home's price based on its attributes, such as number of bedrooms, bathrooms, Square Feet (Sqft) and Zip code or Region. The user enters their requirements into the input form, and predicts the price based on its attributes. This feature was accomplished using Python.The dashboard will also display similar homes (the same number of bedrooms, with ±1 bathroom, ±500 sqft, and in the same zip code or region.) This feature was created with SQL. The Supplemental Data Sheet allows the user to explore different regions and get a sense of how house prices and availability have changed over time.