def run_feasibility(parcels,
parcel_price_callback,
parcel_use_allowed_callback,
residential_to_yearly=True):
"""
Execute development feasibility on all parcels
Parameters
----------
parcels : DataFrame Wrapper
The data frame wrapper for the parcel data
parcel_price_callback : function
A callback which takes each use of the pro forma and returns a series
with index as parcel_id and value as yearly_rent
parcel_use_allowed_callback : function
A callback which takes each form of the pro forma and returns a series
with index as parcel_id and value and boolean whether the form
is allowed on the parcel
residential_to_yearly : boolean (default true)
Whether to use the cap rate to convert the residential price from total
sales price per sqft to rent per sqft
Returns
-------
Adds a table called feasibility to the sim object (returns nothing)
"""
pf = sqftproforma.SqFtProForma()
df = parcels.to_frame()
# add prices for each use
for use in pf.config.uses:
df[use] = parcel_price_callback(use)
# convert from cost to yearly rent
if residential_to_yearly:
df["residential"] *= pf.config.cap_rate
print "Describe of the yearly rent by use"
print df[pf.config.uses].describe()
d = {}
for form in pf.config.forms:
print "Computing feasibility for form %s" % form
d[form] = pf.lookup(form, df[parcel_use_allowed_callback(form)])
far_predictions = pd.concat(d.values(), keys=d.keys(), axis=1)
sim.add_table("feasibility", far_predictions)
def nonresidential_proforma(form, devtype_id, use, parking_rate):
print form
parcels = sim.get_table('parcels').to_frame()
residential_to_yearly = False
parcel_filter = settings['feasibility']['parcel_filter']
#parcel_filter = None
pfc = sqftproforma.SqFtProFormaConfig()
pfc.forms = {form: {use: 1.0}}
pfc.uses = [use]
pfc.residential_uses = [False]
pfc.parking_rates = {use: parking_rate}
if use == 'retail':
pfc.costs = {use: [160.0, 175.0, 200.0, 230.0]}
elif use == 'industrial':
pfc.costs = {use: [140.0, 175.0, 200.0, 230.0]}
else: #office
pfc.costs = {use: [160.0, 175.0, 200.0, 230.0]}
#Fees
fee_schedule_devtype = fee_schedule[fee_schedule.development_type_id ==
devtype_id]
parcel_fee_schedule_devtype = pd.merge(parcel_fee_schedule,
fee_schedule_devtype,
left_on='fee_schedule_id',
right_on='fee_schedule_id')
parcel_fee_schedule_devtype[
'development_fee_per_unit'] = parcel_fee_schedule_devtype.development_fee_per_unit_space_initial * parcel_fee_schedule_devtype.portion
parcel_fees_processed = parcel_fee_schedule_devtype.groupby(
'parcel_id').development_fee_per_unit.sum()
fees = pd.Series(data=parcel_fees_processed,
index=parcels.index).fillna(0)
pf = sqftproforma.SqFtProForma(pfc)
fees = fees * pf.config.cap_rate
return run_proforma_lookup(parcels,
fees,
pf,
use,
form,
residential_to_yearly,
parcel_filter=parcel_filter)
def run_feasibility(parcels,
parcel_price_callback,
parcel_use_allowed_callback,
residential_to_yearly=True,
parcel_filter=None,
only_built=True,
forms_to_test=None,
config=None,
pass_through=[],
simple_zoning=False):
"""
Execute development feasibility on all parcels
Parameters
----------
parcels : DataFrame Wrapper
The data frame wrapper for the parcel data
parcel_price_callback : function
A callback which takes each use of the pro forma and returns a series
with index as parcel_id and value as yearly_rent
parcel_use_allowed_callback : function
A callback which takes each form of the pro forma and returns a series
with index as parcel_id and value and boolean whether the form
is allowed on the parcel
residential_to_yearly : boolean (default true)
Whether to use the cap rate to convert the residential price from total
sales price per sqft to rent per sqft
parcel_filter : string
A filter to apply to the parcels data frame to remove parcels from
consideration - is typically used to remove parcels with buildings
older than a certain date for historical preservation, but is
generally useful
only_built : boolean
Only return those buildings that are profitable - only those buildings
that "will be built"
forms_to_test : list of strings (optional)
Pass the list of the names of forms to test for feasibility - if set to
None will use all the forms available in ProFormaConfig
config : SqFtProFormaConfig configuration object. Optional. Defaults to
None
pass_through : list of strings
Will be passed to the feasibility lookup function - is used to pass
variables from the parcel dataframe to the output dataframe, usually
for debugging
simple_zoning: boolean, optional
This can be set to use only max_dua for residential and max_far for
non-residential. This can be handy if you want to deal with zoning
outside of the developer model.
Returns
-------
Adds a table called feasibility to the sim object (returns nothing)
"""
pf = sqftproforma.SqFtProForma(config) if config \
else sqftproforma.SqFtProForma()
df = parcels.to_frame()
if parcel_filter:
df = df.query(parcel_filter)
# add prices for each use
for use in pf.config.uses:
# assume we can get the 80th percentile price for new development
df[use] = parcel_price_callback(use)
# convert from cost to yearly rent
if residential_to_yearly:
df["residential"] *= pf.config.cap_rate
print "Describe of the yearly rent by use"
print df[pf.config.uses].describe()
d = {}
forms = forms_to_test or pf.config.forms
for form in forms:
print "Computing feasibility for form %s" % form
allowed = parcel_use_allowed_callback(form).loc[df.index]
newdf = df[allowed]
if simple_zoning:
if form == "residential":
# these are new computed in the effective max_dua method
newdf["max_far"] = pd.Series()
newdf["max_height"] = pd.Series()
else:
# these are new computed in the effective max_far method
newdf["max_dua"] = pd.Series()
newdf["max_height"] = pd.Series()
d[form] = pf.lookup(form,
newdf,
only_built=only_built,
pass_through=pass_through)
if residential_to_yearly and "residential" in pass_through:
d[form]["residential"] /= pf.config.cap_rate
far_predictions = pd.concat(d.values(), keys=d.keys(), axis=1)
orca.add_table("feasibility", far_predictions)
def run_feasibility(parcels, parcel_price_callback,
parcel_use_allowed_callback, residential_to_yearly=True,
parcel_filter=None, only_built=True, forms_to_test=None,
config=None, pass_through=[]):
"""
Execute development feasibility on all parcels
Parameters
----------
parcels : DataFrame Wrapper
The data frame wrapper for the parcel data
parcel_price_callback : function
A callback which takes each use of the pro forma and returns a series
with index as parcel_id and value as yearly_rent
parcel_use_allowed_callback : function
A callback which takes each form of the pro forma and returns a series
with index as parcel_id and value and boolean whether the form
is allowed on the parcel
residential_to_yearly : boolean (default true)
Whether to use the cap rate to convert the residential price from total
sales price per sqft to rent per sqft
parcel_filter : string
A filter to apply to the parcels data frame to remove parcels from
consideration - is typically used to remove parcels with buildings
older than a certain date for historical preservation, but is
generally useful
only_built : boolean
Only return those buildings that are profitable - only those buildings
that "will be built"
forms_to_test : list of strings (optional)
Pass the list of the names of forms to test for feasibility - if set to
None will use all the forms available in ProFormaConfig
config : SqFtProFormaConfig configuration object. Optional. Defaults to
None
pass_through : list of strings
Will be passed to the feasibility lookup function - is used to pass
variables from the parcel dataframe to the output dataframe, usually
for debugging
Returns
-------
Adds a table called feasibility to the sim object (returns nothing)
"""
pf = sqftproforma.SqFtProForma(config) if config \
else sqftproforma.SqFtProForma()
df = parcels.to_frame()
if parcel_filter:
df = df.query(parcel_filter)
#print df.loc[765403]
#df.to_csv("select_parcels.csv")
# add prices for each use
for use in pf.config.uses:
# assume we can get the 80th percentile price for new development
df[use] = parcel_price_callback(use)
# convert from cost to yearly rent
if residential_to_yearly:
df["residential"] *= pf.config.cap_rate
print "Describe of the yearly rent by use"
print df[pf.config.uses].describe()
d = {}
forms = forms_to_test or pf.config.forms
for form in forms:
print "Computing feasibility for form %s" % form
allowed = parcel_use_allowed_callback(form).loc[df.index]
#allowed.to_csv(str(form) + "allow.csv")
#df[allowed].to_csv(str(form) + "allow.csv")
d[form] = pf.lookup(form, df[allowed], only_built=only_built,
pass_through=pass_through)
#d[form].to_csv(str(form) + "dform.csv")
if residential_to_yearly and "residential" in pass_through:
d[form]["residential"] /= pf.config.cap_rate
far_predictions = pd.concat(d.values(), keys=d.keys(), axis=1)
far_predictions['residential'].to_csv("residential_far_prediction.csv")
far_predictions['retail'].to_csv("retail_far_prediction.csv")
far_predictions['office'].to_csv("office_far_prediction.csv")
#far_predictions.to_csv("far_prediction.csv")
far_predictions['residential'].max_profit = far_predictions['residential'].max_profit / np.power(far_predictions['residential'].max_profit_far*far_predictions['residential'].shape_area,1)
far_predictions['industrial'].max_profit = far_predictions['industrial'].max_profit / np.power(far_predictions['industrial'].max_profit_far*far_predictions['industrial'].shape_area,1)
far_predictions['retail'].max_profit = far_predictions['retail'].max_profit / np.power(far_predictions['retail'].max_profit_far*far_predictions['retail'].shape_area,1)
far_predictions['office'].max_profit = far_predictions['office'].max_profit / np.power(far_predictions['office'].max_profit_far*far_predictions['office'].shape_area,1)
#far_predictions['residential'].max_profit = np.divide(far_predictions['residential'].max_profit,far_predictions['residential'].max_dua)
#far_predictions['residential'].max_profit[far_predictions['residential'].max_profit==-np.inf] = np.nan
sim.add_table("feasibility", far_predictions)